# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from ppdet.core.workspace import register
from ppdet.modeling.bbox_utils import nonempty_bbox, rbox2poly
from ppdet.modeling.layers import TTFBox
from .transformers import bbox_cxcywh_to_xyxy
try:
from collections.abc import Sequence
except Exception:
from collections import Sequence
__all__ = [
'BBoxPostProcess', 'MaskPostProcess', 'FCOSPostProcess',
'S2ANetBBoxPostProcess', 'JDEBBoxPostProcess', 'CenterNetPostProcess',
'DETRBBoxPostProcess', 'SparsePostProcess'
]
@register
class BBoxPostProcess(nn.Layer):
__shared__ = ['num_classes']
__inject__ = ['decode', 'nms']
def __init__(self, num_classes=80, decode=None, nms=None):
super(BBoxPostProcess, self).__init__()
self.num_classes = num_classes
self.decode = decode
self.nms = nms
self.fake_bboxes = paddle.to_tensor(
np.array(
[[-1, 0.0, 0.0, 0.0, 0.0, 0.0]], dtype='float32'))
self.fake_bbox_num = paddle.to_tensor(np.array([1], dtype='int32'))
def forward(self, head_out, rois, im_shape, scale_factor):
"""
Decode the bbox and do NMS if needed.
Args:
head_out (tuple): bbox_pred and cls_prob of bbox_head output.
rois (tuple): roi and rois_num of rpn_head output.
im_shape (Tensor): The shape of the input image.
scale_factor (Tensor): The scale factor of the input image.
Returns:
bbox_pred (Tensor): The output prediction with shape [N, 6], including
labels, scores and bboxes. The size of bboxes are corresponding
to the input image, the bboxes may be used in other branch.
bbox_num (Tensor): The number of prediction boxes of each batch with
shape [1], and is N.
"""
if self.nms is not None:
bboxes, score = self.decode(head_out, rois, im_shape, scale_factor)
bbox_pred, bbox_num, _ = self.nms(bboxes, score, self.num_classes)
else:
bbox_pred, bbox_num = self.decode(head_out, rois, im_shape,
scale_factor)
return bbox_pred, bbox_num
def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
"""
Rescale, clip and filter the bbox from the output of NMS to
get final prediction.
Notes:
Currently only support bs = 1.
Args:
bboxes (Tensor): The output bboxes with shape [N, 6] after decode
and NMS, including labels, scores and bboxes.
bbox_num (Tensor): The number of prediction boxes of each batch with
shape [1], and is N.
im_shape (Tensor): The shape of the input image.
scale_factor (Tensor): The scale factor of the input image.
Returns:
pred_result (Tensor): The final prediction results with shape [N, 6]
including labels, scores and bboxes.
"""
bboxes_list = []
bbox_num_list = []
id_start = 0
# add fake bbox when output is empty for each batch
for i in range(bbox_num.shape[0]):
if bbox_num[i] == 0:
bboxes_i = self.fake_bboxes
bbox_num_i = self.fake_bbox_num
id_start += 1
else:
bboxes_i = bboxes[id_start:id_start + bbox_num[i], :]
bbox_num_i = bbox_num[i]
id_start += bbox_num[i]
bboxes_list.append(bboxes_i)
bbox_num_list.append(bbox_num_i)
bboxes = paddle.concat(bboxes_list)
bbox_num = paddle.concat(bbox_num_list)
origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
origin_shape_list = []
scale_factor_list = []
# scale_factor: scale_y, scale_x
for i in range(bbox_num.shape[0]):
expand_shape = paddle.expand(origin_shape[i:i + 1, :],
[bbox_num[i], 2])
scale_y, scale_x = scale_factor[i][0], scale_factor[i][1]
scale = paddle.concat([scale_x, scale_y, scale_x, scale_y])
expand_scale = paddle.expand(scale, [bbox_num[i], 4])
origin_shape_list.append(expand_shape)
scale_factor_list.append(expand_scale)
self.origin_shape_list = paddle.concat(origin_shape_list)
scale_factor_list = paddle.concat(scale_factor_list)
# bboxes: [N, 6], label, score, bbox
pred_label = bboxes[:, 0:1]
pred_score = bboxes[:, 1:2]
pred_bbox = bboxes[:, 2:]
# rescale bbox to original image
scaled_bbox = pred_bbox / scale_factor_list
origin_h = self.origin_shape_list[:, 0]
origin_w = self.origin_shape_list[:, 1]
zeros = paddle.zeros_like(origin_h)
# clip bbox to [0, original_size]
x1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 0], origin_w), zeros)
y1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 1], origin_h), zeros)
x2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 2], origin_w), zeros)
y2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 3], origin_h), zeros)
pred_bbox = paddle.stack([x1, y1, x2, y2], axis=-1)
# filter empty bbox
keep_mask = nonempty_bbox(pred_bbox, return_mask=True)
keep_mask = paddle.unsqueeze(keep_mask, [1])
pred_label = paddle.where(keep_mask, pred_label,
paddle.ones_like(pred_label) * -1)
pred_result = paddle.concat([pred_label, pred_score, pred_bbox], axis=1)
return pred_result
def get_origin_shape(self, ):
return self.origin_shape_list
@register
class MaskPostProcess(object):
"""
refer to:
https://github.com/facebookresearch/detectron2/layers/mask_ops.py
Get Mask output according to the output from model
"""
def __init__(self, binary_thresh=0.5):
super(MaskPostProcess, self).__init__()
self.binary_thresh = binary_thresh
def paste_mask(self, masks, boxes, im_h, im_w):
"""
Paste the mask prediction to the original image.
"""
x0, y0, x1, y1 = paddle.split(boxes, 4, axis=1)
masks = paddle.unsqueeze(masks, [0, 1])
img_y = paddle.arange(0, im_h, dtype='float32') + 0.5
img_x = paddle.arange(0, im_w, dtype='float32') + 0.5
img_y = (img_y - y0) / (y1 - y0) * 2 - 1
img_x = (img_x - x0) / (x1 - x0) * 2 - 1
img_x = paddle.unsqueeze(img_x, [1])
img_y = paddle.unsqueeze(img_y, [2])
N = boxes.shape[0]
gx = paddle.expand(img_x, [N, img_y.shape[1], img_x.shape[2]])
gy = paddle.expand(img_y, [N, img_y.shape[1], img_x.shape[2]])
grid = paddle.stack([gx, gy], axis=3)
img_masks = F.grid_sample(masks, grid, align_corners=False)
return img_masks[:, 0]
def __call__(self, mask_out, bboxes, bbox_num, origin_shape):
"""
Decode the mask_out and paste the mask to the origin image.
Args:
mask_out (Tensor): mask_head output with shape [N, 28, 28].
bbox_pred (Tensor): The output bboxes with shape [N, 6] after decode
and NMS, including labels, scores and bboxes.
bbox_num (Tensor): The number of prediction boxes of each batch with
shape [1], and is N.
origin_shape (Tensor): The origin shape of the input image, the tensor
shape is [N, 2], and each row is [h, w].
Returns:
pred_result (Tensor): The final prediction mask results with shape
[N, h, w] in binary mask style.
"""
num_mask = mask_out.shape[0]
origin_shape = paddle.cast(origin_shape, 'int32')
# TODO: support bs > 1 and mask output dtype is bool
pred_result = paddle.zeros(
[num_mask, origin_shape[0][0], origin_shape[0][1]], dtype='int32')
if bbox_num == 1 and bboxes[0][0] == -1:
return pred_result
# TODO: optimize chunk paste
pred_result = []
for i in range(bboxes.shape[0]):
im_h, im_w = origin_shape[i][0], origin_shape[i][1]
pred_mask = self.paste_mask(mask_out[i], bboxes[i:i + 1, 2:], im_h,
im_w)
pred_mask = pred_mask >= self.binary_thresh
pred_mask = paddle.cast(pred_mask, 'int32')
pred_result.append(pred_mask)
pred_result = paddle.concat(pred_result)
return pred_result
@register
class FCOSPostProcess(object):
__inject__ = ['decode', 'nms']
def __init__(self, decode=None, nms=None):
super(FCOSPostProcess, self).__init__()
self.decode = decode
self.nms = nms
def __call__(self, fcos_head_outs, scale_factor):
"""
Decode the bbox and do NMS in FCOS.
"""
locations, cls_logits, bboxes_reg, centerness = fcos_head_outs
bboxes, score = self.decode(locations, cls_logits, bboxes_reg,
centerness, scale_factor)
bbox_pred, bbox_num, _ = self.nms(bboxes, score)
return bbox_pred, bbox_num
@register
class S2ANetBBoxPostProcess(nn.Layer):
__shared__ = ['num_classes']
__inject__ = ['nms']
def __init__(self, num_classes=15, nms_pre=2000, min_bbox_size=0, nms=None):
super(S2ANetBBoxPostProcess, self).__init__()
self.num_classes = num_classes
self.nms_pre = paddle.to_tensor(nms_pre)
self.min_bbox_size = min_bbox_size
self.nms = nms
self.origin_shape_list = []
self.fake_pred_cls_score_bbox = paddle.to_tensor(
np.array(
[[-1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]],
dtype='float32'))
self.fake_bbox_num = paddle.to_tensor(np.array([1], dtype='int32'))
def forward(self, pred_scores, pred_bboxes):
"""
pred_scores : [N, M] score
pred_bboxes : [N, 5] xc, yc, w, h, a
im_shape : [N, 2] im_shape
scale_factor : [N, 2] scale_factor
"""
pred_ploys0 = rbox2poly(pred_bboxes)
pred_ploys = paddle.unsqueeze(pred_ploys0, axis=0)
# pred_scores [NA, 16] --> [16, NA]
pred_scores0 = paddle.transpose(pred_scores, [1, 0])
pred_scores = paddle.unsqueeze(pred_scores0, axis=0)
pred_cls_score_bbox, bbox_num, _ = self.nms(pred_ploys, pred_scores,
self.num_classes)
# Prevent empty bbox_pred from decode or NMS.
# Bboxes and score before NMS may be empty due to the score threshold.
if pred_cls_score_bbox.shape[0] <= 0 or pred_cls_score_bbox.shape[
1] <= 1:
pred_cls_score_bbox = self.fake_pred_cls_score_bbox
bbox_num = self.fake_bbox_num
pred_cls_score_bbox = paddle.reshape(pred_cls_score_bbox, [-1, 10])
return pred_cls_score_bbox, bbox_num
def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
"""
Rescale, clip and filter the bbox from the output of NMS to
get final prediction.
Args:
bboxes(Tensor): bboxes [N, 10]
bbox_num(Tensor): bbox_num
im_shape(Tensor): [1 2]
scale_factor(Tensor): [1 2]
Returns:
bbox_pred(Tensor): The output is the prediction with shape [N, 8]
including labels, scores and bboxes. The size of
bboxes are corresponding to the original image.
"""
origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
origin_shape_list = []
scale_factor_list = []
# scale_factor: scale_y, scale_x
for i in range(bbox_num.shape[0]):
expand_shape = paddle.expand(origin_shape[i:i + 1, :],
[bbox_num[i], 2])
scale_y, scale_x = scale_factor[i][0], scale_factor[i][1]
scale = paddle.concat([
scale_x, scale_y, scale_x, scale_y, scale_x, scale_y, scale_x,
scale_y
])
expand_scale = paddle.expand(scale, [bbox_num[i], 8])
origin_shape_list.append(expand_shape)
scale_factor_list.append(expand_scale)
origin_shape_list = paddle.concat(origin_shape_list)
scale_factor_list = paddle.concat(scale_factor_list)
# bboxes: [N, 10], label, score, bbox
pred_label_score = bboxes[:, 0:2]
pred_bbox = bboxes[:, 2:]
# rescale bbox to original image
pred_bbox = pred_bbox.reshape([-1, 8])
scaled_bbox = pred_bbox / scale_factor_list
origin_h = origin_shape_list[:, 0]
origin_w = origin_shape_list[:, 1]
bboxes = scaled_bbox
zeros = paddle.zeros_like(origin_h)
x1 = paddle.maximum(paddle.minimum(bboxes[:, 0], origin_w - 1), zeros)
y1 = paddle.maximum(paddle.minimum(bboxes[:, 1], origin_h - 1), zeros)
x2 = paddle.maximum(paddle.minimum(bboxes[:, 2], origin_w - 1), zeros)
y2 = paddle.maximum(paddle.minimum(bboxes[:, 3], origin_h - 1), zeros)
x3 = paddle.maximum(paddle.minimum(bboxes[:, 4], origin_w - 1), zeros)
y3 = paddle.maximum(paddle.minimum(bboxes[:, 5], origin_h - 1), zeros)
x4 = paddle.maximum(paddle.minimum(bboxes[:, 6], origin_w - 1), zeros)
y4 = paddle.maximum(paddle.minimum(bboxes[:, 7], origin_h - 1), zeros)
pred_bbox = paddle.stack([x1, y1, x2, y2, x3, y3, x4, y4], axis=-1)
pred_result = paddle.concat([pred_label_score, pred_bbox], axis=1)
return pred_result
@register
class JDEBBoxPostProcess(nn.Layer):
__shared__ = ['num_classes']
__inject__ = ['decode', 'nms']
def __init__(self, num_classes=1, decode=None, nms=None, return_idx=True):
super(JDEBBoxPostProcess, self).__init__()
self.num_classes = num_classes
self.decode = decode
self.nms = nms
self.return_idx = return_idx
self.fake_bbox_pred = paddle.to_tensor(
np.array(
[[-1, 0.0, 0.0, 0.0, 0.0, 0.0]], dtype='float32'))
self.fake_bbox_num = paddle.to_tensor(np.array([1], dtype='int32'))
self.fake_nms_keep_idx = paddle.to_tensor(
np.array(
[[0]], dtype='int32'))
self.fake_yolo_boxes_out = paddle.to_tensor(
np.array(
[[[0.0, 0.0, 0.0, 0.0]]], dtype='float32'))
self.fake_yolo_scores_out = paddle.to_tensor(
np.array(
[[[0.0]]], dtype='float32'))
self.fake_boxes_idx = paddle.to_tensor(np.array([[0]], dtype='int64'))
def forward(self, head_out, anchors):
"""
Decode the bbox and do NMS for JDE model.
Args:
head_out (list): Bbox_pred and cls_prob of bbox_head output.
anchors (list): Anchors of JDE model.
Returns:
boxes_idx (Tensor): The index of kept bboxes after decode 'JDEBox'.
bbox_pred (Tensor): The output is the prediction with shape [N, 6]
including labels, scores and bboxes.
bbox_num (Tensor): The number of prediction of each batch with shape [N].
nms_keep_idx (Tensor): The index of kept bboxes after NMS.
"""
boxes_idx, yolo_boxes_scores = self.decode(head_out, anchors)
if len(boxes_idx) == 0:
boxes_idx = self.fake_boxes_idx
yolo_boxes_out = self.fake_yolo_boxes_out
yolo_scores_out = self.fake_yolo_scores_out
else:
yolo_boxes = paddle.gather_nd(yolo_boxes_scores, boxes_idx)
# TODO: only support bs=1 now
yolo_boxes_out = paddle.reshape(
yolo_boxes[:, :4], shape=[1, len(boxes_idx), 4])
yolo_scores_out = paddle.reshape(
yolo_boxes[:, 4:5], shape=[1, 1, len(boxes_idx)])
boxes_idx = boxes_idx[:, 1:]
if self.return_idx:
bbox_pred, bbox_num, nms_keep_idx = self.nms(
yolo_boxes_out, yolo_scores_out, self.num_classes)
if bbox_pred.shape[0] == 0:
bbox_pred = self.fake_bbox_pred
bbox_num = self.fake_bbox_num
nms_keep_idx = self.fake_nms_keep_idx
return boxes_idx, bbox_pred, bbox_num, nms_keep_idx
else:
bbox_pred, bbox_num, _ = self.nms(yolo_boxes_out, yolo_scores_out,
self.num_classes)
if bbox_pred.shape[0] == 0:
bbox_pred = self.fake_bbox_pred
bbox_num = self.fake_bbox_num
return _, bbox_pred, bbox_num, _
@register
class CenterNetPostProcess(TTFBox):
"""
Postprocess the model outputs to get final prediction:
1. Do NMS for heatmap to get top `max_per_img` bboxes.
2. Decode bboxes using center offset and box size.
3. Rescale decoded bboxes reference to the origin image shape.
Args:
max_per_img(int): the maximum number of predicted objects in a image,
500 by default.
down_ratio(int): the down ratio from images to heatmap, 4 by default.
regress_ltrb (bool): whether to regress left/top/right/bottom or
width/height for a box, true by default.
for_mot (bool): whether return other features used in tracking model.
"""
__shared__ = ['down_ratio', 'for_mot']
def __init__(self,
max_per_img=500,
down_ratio=4,
regress_ltrb=True,
for_mot=False):
super(TTFBox, self).__init__()
self.max_per_img = max_per_img
self.down_ratio = down_ratio
self.regress_ltrb = regress_ltrb
self.for_mot = for_mot
def __call__(self, hm, wh, reg, im_shape, scale_factor):
heat = self._simple_nms(hm)
scores, inds, topk_clses, ys, xs = self._topk(heat)
scores = scores.unsqueeze(1)
clses = topk_clses.unsqueeze(1)
reg_t = paddle.transpose(reg, [0, 2, 3, 1])
# Like TTFBox, batch size is 1.
# TODO: support batch size > 1
reg = paddle.reshape(reg_t, [-1, reg_t.shape[-1]])
reg = paddle.gather(reg, inds)
xs = paddle.cast(xs, 'float32')
ys = paddle.cast(ys, 'float32')
xs = xs + reg[:, 0:1]
ys = ys + reg[:, 1:2]
wh_t = paddle.transpose(wh, [0, 2, 3, 1])
wh = paddle.reshape(wh_t, [-1, wh_t.shape[-1]])
wh = paddle.gather(wh, inds)
if self.regress_ltrb:
x1 = xs - wh[:, 0:1]
y1 = ys - wh[:, 1:2]
x2 = xs + wh[:, 2:3]
y2 = ys + wh[:, 3:4]
else:
x1 = xs - wh[:, 0:1] / 2
y1 = ys - wh[:, 1:2] / 2
x2 = xs + wh[:, 0:1] / 2
y2 = ys + wh[:, 1:2] / 2
n, c, feat_h, feat_w = hm.shape[:]
padw = (feat_w * self.down_ratio - im_shape[0, 1]) / 2
padh = (feat_h * self.down_ratio - im_shape[0, 0]) / 2
x1 = x1 * self.down_ratio
y1 = y1 * self.down_ratio
x2 = x2 * self.down_ratio
y2 = y2 * self.down_ratio
x1 = x1 - padw
y1 = y1 - padh
x2 = x2 - padw
y2 = y2 - padh
bboxes = paddle.concat([x1, y1, x2, y2], axis=1)
scale_y = scale_factor[:, 0:1]
scale_x = scale_factor[:, 1:2]
scale_expand = paddle.concat(
[scale_x, scale_y, scale_x, scale_y], axis=1)
boxes_shape = bboxes.shape[:]
scale_expand = paddle.expand(scale_expand, shape=boxes_shape)
bboxes = paddle.divide(bboxes, scale_expand)
if self.for_mot:
results = paddle.concat([bboxes, scores, clses], axis=1)
return results, inds, topk_clses
else:
results = paddle.concat([clses, scores, bboxes], axis=1)
return results, paddle.shape(results)[0:1], topk_clses
@register
class DETRBBoxPostProcess(object):
__shared__ = ['num_classes', 'use_focal_loss']
__inject__ = []
def __init__(self,
num_classes=80,
num_top_queries=100,
use_focal_loss=False):
super(DETRBBoxPostProcess, self).__init__()
self.num_classes = num_classes
self.num_top_queries = num_top_queries
self.use_focal_loss = use_focal_loss
def __call__(self, head_out, im_shape, scale_factor):
"""
Decode the bbox.
Args:
head_out (tuple): bbox_pred, cls_logit and masks of bbox_head output.
im_shape (Tensor): The shape of the input image.
scale_factor (Tensor): The scale factor of the input image.
Returns:
bbox_pred (Tensor): The output prediction with shape [N, 6], including
labels, scores and bboxes. The size of bboxes are corresponding
to the input image, the bboxes may be used in other branch.
bbox_num (Tensor): The number of prediction boxes of each batch with
shape [bs], and is N.
"""
bboxes, logits, masks = head_out
bbox_pred = bbox_cxcywh_to_xyxy(bboxes)
origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
img_h, img_w = origin_shape.unbind(1)
origin_shape = paddle.stack(
[img_w, img_h, img_w, img_h], axis=-1).unsqueeze(0)
bbox_pred *= origin_shape
scores = F.sigmoid(logits) if self.use_focal_loss else F.softmax(
logits)[:, :, :-1]
if not self.use_focal_loss:
scores, labels = scores.max(-1), scores.argmax(-1)
if scores.shape[1] > self.num_top_queries:
scores, index = paddle.topk(
scores, self.num_top_queries, axis=-1)
labels = paddle.stack(
[paddle.gather(l, i) for l, i in zip(labels, index)])
bbox_pred = paddle.stack(
[paddle.gather(b, i) for b, i in zip(bbox_pred, index)])
else:
scores, index = paddle.topk(
scores.reshape([logits.shape[0], -1]),
self.num_top_queries,
axis=-1)
labels = index % logits.shape[2]
index = index // logits.shape[2]
bbox_pred = paddle.stack(
[paddle.gather(b, i) for b, i in zip(bbox_pred, index)])
bbox_pred = paddle.concat(
[
labels.unsqueeze(-1).astype('float32'), scores.unsqueeze(-1),
bbox_pred
],
axis=-1)
bbox_num = paddle.to_tensor(
bbox_pred.shape[1], dtype='int32').tile([bbox_pred.shape[0]])
bbox_pred = bbox_pred.reshape([-1, 6])
return bbox_pred, bbox_num
@register
class SparsePostProcess(object):
__shared__ = ['num_classes']
def __init__(self, num_proposals, num_classes=80):
super(SparsePostProcess, self).__init__()
self.num_classes = num_classes
self.num_proposals = num_proposals
def __call__(self, box_cls, box_pred, scale_factor_wh, img_whwh):
"""
Arguments:
box_cls (Tensor): tensor of shape (batch_size, num_proposals, K).
The tensor predicts the classification probability for each proposal.
box_pred (Tensor): tensors of shape (batch_size, num_proposals, 4).
The tensor predicts 4-vector (x,y,w,h) box
regression values for every proposal
scale_factor_wh (Tensor): tensors of shape [batch_size, 2] the scalor of per img
img_whwh (Tensor): tensors of shape [batch_size, 4]
Returns:
bbox_pred (Tensor): tensors of shape [num_boxes, 6] Each row has 6 values:
[label, confidence, xmin, ymin, xmax, ymax]
bbox_num (Tensor): tensors of shape [batch_size] the number of RoIs in each image.
"""
assert len(box_cls) == len(scale_factor_wh) == len(img_whwh)
img_wh = img_whwh[:, :2]
scores = F.sigmoid(box_cls)
labels = paddle.arange(0, self.num_classes). \
unsqueeze(0).tile([self.num_proposals, 1]).flatten(start_axis=0, stop_axis=1)
classes_all = []
scores_all = []
boxes_all = []
for i, (scores_per_image,
box_pred_per_image) in enumerate(zip(scores, box_pred)):
scores_per_image, topk_indices = scores_per_image.flatten(
0, 1).topk(
self.num_proposals, sorted=False)
labels_per_image = paddle.gather(labels, topk_indices, axis=0)
box_pred_per_image = box_pred_per_image.reshape([-1, 1, 4]).tile(
[1, self.num_classes, 1]).reshape([-1, 4])
box_pred_per_image = paddle.gather(
box_pred_per_image, topk_indices, axis=0)
classes_all.append(labels_per_image)
scores_all.append(scores_per_image)
boxes_all.append(box_pred_per_image)
bbox_num = paddle.zeros([len(scale_factor_wh)], dtype="int32")
boxes_final = []
for i in range(len(scale_factor_wh)):
classes = classes_all[i]
boxes = boxes_all[i]
scores = scores_all[i]
boxes[:, 0::2] = paddle.clip(
boxes[:, 0::2], min=0, max=img_wh[i][0]) / scale_factor_wh[i][0]
boxes[:, 1::2] = paddle.clip(
boxes[:, 1::2], min=0, max=img_wh[i][1]) / scale_factor_wh[i][1]
boxes_w, boxes_h = (boxes[:, 2] - boxes[:, 0]).numpy(), (
boxes[:, 3] - boxes[:, 1]).numpy()
keep = (boxes_w > 1.) & (boxes_h > 1.)
if (keep.sum() == 0):
bboxes = paddle.zeros([1, 6]).astype("float32")
else:
boxes = paddle.to_tensor(boxes.numpy()[keep]).astype("float32")
classes = paddle.to_tensor(classes.numpy()[keep]).astype(
"float32").unsqueeze(-1)
scores = paddle.to_tensor(scores.numpy()[keep]).astype(
"float32").unsqueeze(-1)
bboxes = paddle.concat([classes, scores, boxes], axis=-1)
boxes_final.append(bboxes)
bbox_num[i] = bboxes.shape[0]
bbox_pred = paddle.concat(boxes_final)
return bbox_pred, bbox_num
def nms(dets, thresh):
"""Apply classic DPM-style greedy NMS."""
if dets.shape[0] == 0:
return dets[[], :]
scores = dets[:, 0]
x1 = dets[:, 1]
y1 = dets[:, 2]
x2 = dets[:, 3]
y2 = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
ndets = dets.shape[0]
suppressed = np.zeros((ndets), dtype=np.int)
# nominal indices
# _i, _j
# sorted indices
# i, j
# temp variables for box i's (the box currently under consideration)
# ix1, iy1, ix2, iy2, iarea
# variables for computing overlap with box j (lower scoring box)
# xx1, yy1, xx2, yy2
# w, h
# inter, ovr
for _i in range(ndets):
i = order[_i]
if suppressed[i] == 1:
continue
ix1 = x1[i]
iy1 = y1[i]
ix2 = x2[i]
iy2 = y2[i]
iarea = areas[i]
for _j in range(_i + 1, ndets):
j = order[_j]
if suppressed[j] == 1:
continue
xx1 = max(ix1, x1[j])
yy1 = max(iy1, y1[j])
xx2 = min(ix2, x2[j])
yy2 = min(iy2, y2[j])
w = max(0.0, xx2 - xx1 + 1)
h = max(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (iarea + areas[j] - inter)
if ovr >= thresh:
suppressed[j] = 1
keep = np.where(suppressed == 0)[0]
dets = dets[keep, :]
return dets