[MOT] add FairMOT (#2994)

* add fairmot

* rename pretrain_weights

* add comments; reuse focal loss and convnorm

* update comments

configs/datasets/mot.yml

@@ -12,7 +12,7 @@ MOTDataZoo: {
 TrainDataset:
   !MOTDataSet
     dataset_dir: dataset/mot
-    image_lists: ['mot17.train', 'caltech.train', 'cuhksysu.train', 'prw.train', 'citypersons.train', 'eth.train']
+    image_lists: ['mot17.train', 'caltech.all', 'cuhksysu.train', 'prw.train', 'citypersons.train', 'eth.train']
     data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
 
 EvalDataset:

configs/mot/fairmot/README.md

+# FairMOT (FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking)
+
+## Table of Contents
+- [Introduction](#Introduction)
+- [Model Zoo](#Model_Zoo)
+- [Getting Start](#Getting_Start)
+- [Citations](#Citations)
+
+## Introduction
+
+FairMOT focuses on accomplishing the detection and re-identification in a single network to improve the inference speed, presents a simple baseline which consists of two homogeneous branches to predict pixel-wise objectness scores and re-ID features. The achieved fairness between the two tasks allows FairMOT to obtain high levels of detection and tracking accuracy.
+
+
+## Model Zoo
+
+### Results on MOT-16 train set
+
+| backbone      | input shape  | MOTA   | IDF1   |  IDS  |   FP  |   FN  | download  | config |
+| :-----------------| :------- | :----: | :----: | :---: | :----: | :---: |:---: | :---: |
+| DLA-34(paper)  | 1088x608 |  83.3  |  81.9  | 544  |  3822  | 14095 | ---- | ---- |
+| DLA-34         | 1088x608 |  83.4  |  82.7  | 517  | 4077   | 13761 |  [model](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml) |
+
+
+### Results on MOT-16 test set
+
+| backbone      | input shape  | MOTA   | IDF1   |  IDS  |   MT  |   ML  | download  | config |
+| :-----------------| :------- | :----: | :----: | :---: | :----: | :---: | :---: | :---: |
+| DLA-34(paper)  | 1088x608 |    74.9   72.8    1074    44.7%   15.9%  | ---- | ---- |
+| DLA-34         | 1088x608 |  74.7  |  72.8  | 1044  | 41.9%   | 19.1% |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml) |
+
+**Notes:**
+
+FairMOT used 2 GPUs for training and mini-batch size as 6 on each GPU, and trained for 30 epoches.
+
+## Getting Start
+
+### 1. Training
+
+Training FairMOT on 2 GPUs with following command
+
+```bash
+python -m paddle.distributed.launch --log_dir=./fairmot_dla34_30e_1088x608/ --gpus 0,1 tools/train.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml &>fairmot_dla34_30e_1088x608.log 2>&1 &
+```
+
+
+### 2. Evaluation
+
+Evaluating the track performance of FairMOT on val dataset in single GPU with following commands:
+
+```bash
+# use weights released in PaddleDetection model zoo
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams
+
+# use saved checkpoint in training
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml -o weights=output/fairmot_dla34_30e_1088x608/model_final
+```
+
+## Citations
+```
+@article{zhang2020fair,
+  title={FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking},
+  author={Zhang, Yifu and Wang, Chunyu and Wang, Xinggang and Zeng, Wenjun and Liu, Wenyu},
+  journal={arXiv preprint arXiv:2004.01888},
+  year={2020}
+}
+```

configs/mot/fairmot/_base_/fairmot_dla34.yml

+architecture: FairMOT
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/fairmot_dla34_crowdhuman_pretrained.pdparams
+
+FairMOT:
+  detector: CenterNet
+  reid: FairMOTEmbeddingHead
+  loss: FairMOTLoss
+  tracker: JDETracker
+
+CenterNet:
+  backbone: DLA
+  neck: CenterNetDLAFPN
+  head: CenterNetHead
+  post_process: CenterNetPostProcess
+  for_mot: True
+
+CenterNetPostProcess:
+  for_mot: True
+
+JDETracker:
+  conf_thres: 0.4
+  tracked_thresh: 0.4
+  metric_type: cosine

configs/mot/fairmot/_base_/fairmot_reader_1088x608.yml

+worker_num: 4
+TrainReader:
+  inputs_def:
+    image_shape: [3, 608, 1088]
+  sample_transforms:
+    - Decode: {to_rgb: False}
+    - AugmentHSV: {is_bgr: True}
+    - LetterBoxResize: {target_size: [608, 1088]}
+    - MOTRandomAffine: {reject_outside: False}
+    - RandomFlip: {}
+    - BboxXYXY2XYWH: {}
+    - NormalizeBox: {}
+    - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1]}
+    - Permute: {to_rgb: True}
+  batch_transforms:
+    - Gt2FairMOTTarget: {}
+  batch_size: 6
+  shuffle: True
+  drop_last: True
+
+
+EvalMOTReader:
+  inputs_def:
+    image_shape: [3, 608, 1088]
+  sample_transforms:
+    - Decode: {to_rgb: False}
+    - LetterBoxResize: {target_size: [608, 1088]}
+    - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1]}
+    - Permute: {to_rgb: True}
+  batch_size: 1

configs/mot/fairmot/_base_/optimizer_30e.yml

+epoch: 30
+
+LearningRate:
+  base_lr: 0.0001
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [20,]
+    use_warmup: False
+
+OptimizerBuilder:
+  optimizer:
+    type: Adam
+  regularizer: NULL

configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml

+_BASE_: [
+  '../../datasets/mot.yml',
+  '../../runtime.yml',
+  '_base_/optimizer_30e.yml',
+  '_base_/fairmot_dla34.yml',
+  '_base_/fairmot_reader_1088x608.yml',
+]
+
+metric: MOT
+weights: output/fairmot_dla34_30e_1088x608/model_final

ppdet/data/source/mot.py

@@ -347,6 +347,7 @@ class MOTVideoDataset(DetDataset):
     def _load_video_images(self):
         self.cap = cv2.VideoCapture(self.video_file)
         self.vn = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        self.frame_rate = int(round(self.cap.get(cv2.CAP_PROP_FPS)))
         logger.info('Length of the video: {:d} frames'.format(self.vn))
         res = True
         ct = 0

ppdet/data/transform/mot_operators.py

@@ -25,14 +25,19 @@ from numbers import Integral
 import cv2
 import copy
 import numpy as np
+import math
 
 from .operators import BaseOperator, register_op
+from .batch_operators import Gt2TTFTarget
 from ppdet.modeling.bbox_utils import bbox_iou_np_expand
 from ppdet.core.workspace import serializable
 from ppdet.utils.logger import setup_logger
 logger = setup_logger(__name__)
 
-__all__ = ['LetterBoxResize', 'Gt2JDETargetThres', 'Gt2JDETargetMax']
+__all__ = [
+    'LetterBoxResize', 'Gt2JDETargetThres', 'Gt2JDETargetMax',
+    'Gt2FairMOTTarget'
+]
 
 
 @register_op
@@ -367,3 +372,117 @@ class Gt2JDETargetMax(BaseOperator):
                 sample['tbox{}'.format(i)] = tbox
                 sample['tconf{}'.format(i)] = tconf
                 sample['tide{}'.format(i)] = tid
+
+
+class Gt2FairMOTTarget(Gt2TTFTarget):
+    __shared__ = ['num_classes']
+    """
+    Generate FairMOT targets by ground truth data.
+    Difference between Gt2FairMOTTarget and Gt2TTFTarget are:
+        1. the gaussian kernal radius to generate a heatmap.
+        2. the targets needed during traing.
+    
+    Args:
+        num_classes(int): the number of classes.
+        down_ratio(int): the down ratio from images to heatmap, 4 by default.
+        max_objs(int): the maximum number of ground truth objects in a image, 500 by default.
+    """
+
+    def __init__(self, num_classes=1, down_ratio=4, max_objs=500):
+        super(Gt2TTFTarget, self).__init__()
+        self.down_ratio = down_ratio
+        self.num_classes = num_classes
+        self.max_objs = max_objs
+
+    def __call__(self, samples, context=None):
+        for b_id, sample in enumerate(samples):
+            output_h = sample['image'].shape[1] // self.down_ratio
+            output_w = sample['image'].shape[2] // self.down_ratio
+
+            heatmap = np.zeros(
+                (self.num_classes, output_h, output_w), dtype='float32')
+            bbox_size = np.zeros((self.max_objs, 4), dtype=np.float32)
+            center_offset = np.zeros((self.max_objs, 2), dtype=np.float32)
+            index = np.zeros((self.max_objs, ), dtype=np.int64)
+            index_mask = np.zeros((self.max_objs, ), dtype=np.int32)
+            reid = np.zeros((self.max_objs, ), dtype=np.int64)
+            bbox_xys = np.zeros((self.max_objs, 4), dtype=np.float32)
+
+            gt_bbox = sample['gt_bbox']
+            gt_class = sample['gt_class']
+            gt_ide = sample['gt_ide']
+
+            for k in range(len(gt_bbox)):
+                cls_id = gt_class[k][0]
+                bbox = gt_bbox[k]
+                ide = gt_ide[k][0]
+                bbox[[0, 2]] = bbox[[0, 2]] * output_w
+                bbox[[1, 3]] = bbox[[1, 3]] * output_h
+                bbox_amodal = copy.deepcopy(bbox)
+                bbox_amodal[0] = bbox_amodal[0] - bbox_amodal[2] / 2.
+                bbox_amodal[1] = bbox_amodal[1] - bbox_amodal[3] / 2.
+                bbox_amodal[2] = bbox_amodal[0] + bbox_amodal[2]
+                bbox_amodal[3] = bbox_amodal[1] + bbox_amodal[3]
+                bbox[0] = np.clip(bbox[0], 0, output_w - 1)
+                bbox[1] = np.clip(bbox[1], 0, output_h - 1)
+                h = bbox[3]
+                w = bbox[2]
+
+                bbox_xy = copy.deepcopy(bbox)
+                bbox_xy[0] = bbox_xy[0] - bbox_xy[2] / 2
+                bbox_xy[1] = bbox_xy[1] - bbox_xy[3] / 2
+                bbox_xy[2] = bbox_xy[0] + bbox_xy[2]
+                bbox_xy[3] = bbox_xy[1] + bbox_xy[3]
+
+                if h > 0 and w > 0:
+                    radius = self.gaussian_radius((math.ceil(h), math.ceil(w)))
+                    radius = max(0, int(radius))
+                    ct = np.array([bbox[0], bbox[1]], dtype=np.float32)
+                    ct_int = ct.astype(np.int32)
+                    self.draw_truncate_gaussian(heatmap[cls_id], ct_int, radius,
+                                                radius)
+                    bbox_size[k] = ct[0] - bbox_amodal[0], ct[1] - bbox_amodal[1], \
+                            bbox_amodal[2] - ct[0], bbox_amodal[3] - ct[1]
+
+                    index[k] = ct_int[1] * output_w + ct_int[0]
+                    center_offset[k] = ct - ct_int
+                    index_mask[k] = 1
+                    reid[k] = ide
+                    bbox_xys[k] = bbox_xy
+
+            sample['heatmap'] = heatmap
+            sample['index'] = index
+            sample['offset'] = center_offset
+            sample['size'] = bbox_size
+            sample['index_mask'] = index_mask
+            sample['reid'] = reid
+            sample['bbox_xys'] = bbox_xys
+            sample.pop('is_crowd', None)
+            sample.pop('difficult', None)
+            sample.pop('gt_class', None)
+            sample.pop('gt_bbox', None)
+            sample.pop('gt_score', None)
+            sample.pop('gt_ide', None)
+        return samples
+
+    def gaussian_radius(self, det_size, min_overlap=0.7):
+        height, width = det_size
+
+        a1 = 1
+        b1 = (height + width)
+        c1 = width * height * (1 - min_overlap) / (1 + min_overlap)
+        sq1 = np.sqrt(b1**2 - 4 * a1 * c1)
+        r1 = (b1 + sq1) / 2
+
+        a2 = 4
+        b2 = 2 * (height + width)
+        c2 = (1 - min_overlap) * width * height
+        sq2 = np.sqrt(b2**2 - 4 * a2 * c2)
+        r2 = (b2 + sq2) / 2
+
+        a3 = 4 * min_overlap
+        b3 = -2 * min_overlap * (height + width)
+        c3 = (min_overlap - 1) * width * height
+        sq3 = np.sqrt(b3**2 - 4 * a3 * c3)
+        r3 = (b3 + sq3) / 2
+        return min(r1, r2, r3)

ppdet/data/transform/operators.py

@@ -107,10 +107,12 @@ class BaseOperator(object):
 
 @register_op
 class Decode(BaseOperator):
-    def __init__(self):
+    def __init__(self, to_rgb=True):
         """ Transform the image data to numpy format following the rgb format
         """
         super(Decode, self).__init__()
+        # TODO: remove this parameter
+        self.to_rgb = to_rgb
 
     def apply(self, sample, context=None):
         """ load image if 'im_file' field is not empty but 'image' is"""
@@ -124,7 +126,8 @@ class Decode(BaseOperator):
         im = cv2.imdecode(data, 1)  # BGR mode, but need RGB mode
         if 'keep_ori_im' in sample and sample['keep_ori_im']:
             sample['ori_image'] = im
-        im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
+        if self.to_rgb:
+            im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
 
         sample['image'] = im
         if 'h' not in sample:
@@ -151,14 +154,18 @@ class Decode(BaseOperator):
 
 @register_op
 class Permute(BaseOperator):
-    def __init__(self):
+    def __init__(self, to_rgb=False):
         """
         Change the channel to be (C, H, W)
         """
         super(Permute, self).__init__()
+        # TODO: remove this parameter
+        self.to_rgb = to_rgb
 
     def apply(self, sample, context=None):
         im = sample['image']
+        if self.to_rgb:
+            im = np.ascontiguousarray(im[:, :, ::-1])
         im = im.transpose((2, 0, 1))
         sample['image'] = im
         return sample
@@ -2076,29 +2083,39 @@ class Norm2PixelBbox(BaseOperator):
 
 @register_op
 class MOTRandomAffine(BaseOperator):
+    """ 
+    Affine transform to image and coords to achieve the rotate, scale and
+    shift effect for training image.
+
+    Args:
+        degrees (list[2]): the rotate range to apply, transform range is [min, max]
+        translate (list[2]): the translate range to apply, ransform range is [min, max]
+        scale (list[2]): the scale range to apply, transform range is [min, max]
+        shear (list[2]): the shear range to apply, transform range is [min, max]
+        borderValue (list[3]): value used in case of a constant border when appling
+            the perspective transformation
+        reject_outside (bool): reject warped bounding bboxes outside of image
+
+    Returns:
+        records(dict): contain the image and coords after tranformed
+
+    """
+
     def __init__(self,
                  degrees=(-5, 5),
                  translate=(0.10, 0.10),
                  scale=(0.50, 1.20),
                  shear=(-2, 2),
-                 borderValue=(127.5, 127.5, 127.5)):
-        """ 
-        Affine transform to image and coords to achieve the rotate, scale and
-        shift effect for training image.
+                 borderValue=(127.5, 127.5, 127.5),
+                 reject_outside=True):
 
-        Args:
-            degrees (tuple): rotation value
-            translate (tuple): xy coords translation value
-            scale (tuple): scale value
-            shear (tuple): shear value
-            borderValue (tuple): border color value
-        """
         super(MOTRandomAffine, self).__init__()
         self.degrees = degrees
         self.translate = translate
         self.scale = scale
         self.shear = shear
         self.borderValue = borderValue
+        self.reject_outside = reject_outside
 
     def apply(self, sample, context=None):
         # https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
@@ -2171,10 +2188,11 @@ class MOTRandomAffine(BaseOperator):
                 (x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
 
             # reject warped points outside of image
-            np.clip(xy[:, 0], 0, width, out=xy[:, 0])
-            np.clip(xy[:, 2], 0, width, out=xy[:, 2])
-            np.clip(xy[:, 1], 0, height, out=xy[:, 1])
-            np.clip(xy[:, 3], 0, height, out=xy[:, 3])
+            if self.reject_outside:
+                np.clip(xy[:, 0], 0, width, out=xy[:, 0])
+                np.clip(xy[:, 2], 0, width, out=xy[:, 2])
+                np.clip(xy[:, 1], 0, height, out=xy[:, 1])
+                np.clip(xy[:, 3], 0, height, out=xy[:, 3])
             w = xy[:, 2] - xy[:, 0]
             h = xy[:, 3] - xy[:, 1]
             area = w * h

ppdet/engine/trainer.py

@@ -66,6 +66,10 @@ class Trainer(object):
             cfg['JDEEmbeddingHead'][
                 'num_identifiers'] = self.dataset.total_identities
 
+        if cfg.architecture == 'FairMOT' and self.mode == 'train':
+            cfg['FairMOTEmbeddingHead'][
+                'num_identifiers'] = self.dataset.total_identities
+
         # build model
         if 'model' not in self.cfg:
             self.model = create(cfg.architecture)
@@ -223,7 +227,10 @@ class Trainer(object):
             return
         self.start_epoch = 0
         if hasattr(self.model, 'detector'):
-            load_pretrain_weight(self.model.detector, weights)
+            if self.model.__class__.__name__ == 'FairMOT':
+                load_pretrain_weight(self.model, weights)
+            else:
+                load_pretrain_weight(self.model.detector, weights)
         else:
             load_pretrain_weight(self.model, weights)
         logger.debug("Load weights {} to start training".format(weights))

ppdet/modeling/architectures/__init__.py

@@ -19,6 +19,8 @@ from . import keypoint_hrhrnet
 from . import keypoint_hrnet
 from . import jde
 from . import deepsort
+from . import fairmot
+from . import centernet
 
 from .meta_arch import *
 from .faster_rcnn import *
@@ -34,3 +36,5 @@ from .keypoint_hrhrnet import *
 from .keypoint_hrnet import *
 from .jde import *
 from .deepsort import *
+from .fairmot import *
+from .centernet import *

ppdet/modeling/architectures/centernet.py

+# Copyright (c) 2021 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from ppdet.core.workspace import register, create
+from .meta_arch import BaseArch
+
+__all__ = ['CenterNet']
+
+
+@register
+class CenterNet(BaseArch):
+    """
+    CenterNet network, see http://arxiv.org/abs/1904.07850
+
+    Args:
+        backbone (object): backbone instance
+        neck (object): 'CenterDLAFPN' instance
+        head (object): 'CenterHead' instance
+        post_process (object): 'CenterNetPostProcess' instance
+        for_mot (bool): whether return other features used in tracking model
+
+    """
+    __category__ = 'architecture'
+    __inject__ = ['post_process']
+
+    def __init__(self,
+                 backbone='DLA',
+                 neck='CenterDLAFPN',
+                 head='CenterHead',
+                 post_process='CenterNetPostProcess',
+                 for_mot=False):
+        super(CenterNet, self).__init__()
+        self.backbone = backbone
+        self.neck = neck
+        self.head = head
+        self.post_process = post_process
+        self.for_mot = for_mot
+
+    @classmethod
+    def from_config(cls, cfg, *args, **kwargs):
+        backbone = create(cfg['backbone'])
+
+        kwargs = {'input_shape': backbone.out_shape}
+        neck = create(cfg['neck'], **kwargs)
+
+        kwargs = {'input_shape': neck.out_shape}
+        head = create(cfg['head'], **kwargs)
+
+        return {'backbone': backbone, 'neck': neck, "head": head}
+
+    def _forward(self):
+        body_feats = self.backbone(self.inputs)
+        neck_feat = self.neck(body_feats)
+        head_out = self.head(neck_feat, self.inputs)
+        if self.for_mot:
+            head_out.update({'neck_feat': neck_feat})
+        return head_out
+
+    def get_pred(self):
+        head_out = self._forward()
+        if self.for_mot:
+            bbox, bbox_inds = self.post_process(
+                head_out['heatmap'],
+                head_out['size'],
+                head_out['offset'],
+                im_shape=self.inputs['im_shape'],
+                scale_factor=self.inputs['scale_factor'])
+            output = {
+                "bbox": bbox,
+                "bbox_inds": bbox_inds,
+                "neck_feat": head_out['neck_feat']
+            }
+        else:
+            bbox, bbox_num = self.post_process(
+                head_out['heatmap'],
+                head_out['size'],
+                head_out['offset'],
+                im_shape=self.inputs['im_shape'],
+                scale_factor=self.inputs['scale_factor'])
+            output = {
+                "bbox": bbox,
+                "bbox_num": bbox_num,
+            }
+        return output
+
+    def get_loss(self):
+        return self._forward()

ppdet/modeling/architectures/fairmot.py

+# Copyright (c) 2021 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from ppdet.core.workspace import register, create
+from .meta_arch import BaseArch
+
+__all__ = ['FairMOT']
+
+
+@register
+class FairMOT(BaseArch):
+    """
+    FairMOT network, see http://arxiv.org/abs/2004.01888
+
+    Args:
+        detector (object): 'CenterNet' instance
+        reid (object): 'FairMOTEmbeddingHead' instance
+        tracker (object): 'JDETracker' instance
+        loss (object): 'FairMOTLoss' instance
+
+    """
+
+    __category__ = 'architecture'
+    __inject__ = ['loss']
+
+    def __init__(self,
+                 detector='CenterNet',
+                 reid='FairMOTEmbeddingHead',
+                 tracker='JDETracker',
+                 loss='FairMOTLoss'):
+        super(FairMOT, self).__init__()
+        self.detector = detector
+        self.reid = reid
+        self.tracker = tracker
+        self.loss = loss
+
+    @classmethod
+    def from_config(cls, cfg, *args, **kwargs):
+        detector = create(cfg['detector'])
+
+        kwargs = {'input_shape': detector.neck.out_shape}
+        reid = create(cfg['reid'], **kwargs)
+        loss = create(cfg['loss'])
+        tracker = create(cfg['tracker'])
+
+        return {
+            'detector': detector,
+            'reid': reid,
+            'loss': loss,
+            'tracker': tracker
+        }
+
+    def _forward(self):
+        loss = dict()
+        # det_outs keys:
+        # train: det_loss, heatmap_loss, size_loss, offset_loss, neck_feat
+        # eval/infer: bbox, bbox_inds, neck_feat
+        det_outs = self.detector(self.inputs)
+        neck_feat = det_outs['neck_feat']
+        if self.training:
+            reid_loss = self.reid(neck_feat, self.inputs)
+
+            det_loss = det_outs['det_loss']
+            loss = self.loss(det_loss, reid_loss)
+            loss.update({
+                'heatmap_loss': det_outs['heatmap_loss'],
+                'size_loss': det_outs['size_loss'],
+                'offset_loss': det_outs['offset_loss'],
+                'reid_loss': reid_loss
+            })
+            return loss
+        else:
+            embedding = self.reid(neck_feat, self.inputs)
+            bbox_inds = det_outs['bbox_inds']
+            embedding = paddle.transpose(embedding, [0, 2, 3, 1])
+            embedding = paddle.reshape(embedding,
+                                       [-1, paddle.shape(embedding)[-1]])
+            id_feature = paddle.gather(embedding, bbox_inds)
+            dets = det_outs['bbox']
+            id_feature = id_feature
+            # Note: the tracker only considers batch_size=1 and num_classses=1
+            online_targets = self.tracker.update(dets, id_feature)
+            return online_targets
+
+    def get_pred(self):
+        output = self._forward()
+        return output
+
+    def get_loss(self):
+        loss = self._forward()
+        return loss

ppdet/modeling/backbones/__init__.py

@@ -22,6 +22,7 @@ from . import blazenet
 from . import ghostnet
 from . import senet
 from . import res2net
+from . import dla
 
 from .vgg import *
 from .resnet import *
@@ -33,3 +34,4 @@ from .blazenet import *
 from .ghostnet import *
 from .senet import *
 from .res2net import *
+from .dla import *

ppdet/modeling/backbones/dla.py

+# Copyright (c) 2021 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 paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from ppdet.core.workspace import register, serializable
+from ppdet.modeling.layers import ConvNormLayer
+from ..shape_spec import ShapeSpec
+
+DLA_cfg = {34: ([1, 1, 1, 2, 2, 1], [16, 32, 64, 128, 256, 512])}
+
+
+class BasicBlock(nn.Layer):
+    def __init__(self, ch_in, ch_out, stride=1):
+        super(BasicBlock, self).__init__()
+        self.conv1 = ConvNormLayer(
+            ch_in,
+            ch_out,
+            filter_size=3,
+            stride=stride,
+            bias_on=False,
+            norm_decay=None)
+        self.conv2 = ConvNormLayer(
+            ch_out,
+            ch_out,
+            filter_size=3,
+            stride=1,
+            bias_on=False,
+            norm_decay=None)
+
+    def forward(self, inputs, residual=None):
+        if residual is None:
+            residual = inputs
+
+        out = self.conv1(inputs)
+        out = F.relu(out)
+
+        out = self.conv2(out)
+
+        out = paddle.add(x=out, y=residual)
+        out = F.relu(out)
+
+        return out
+
+
+class Root(nn.Layer):
+    def __init__(self, ch_in, ch_out, kernel_size, residual):
+        super(Root, self).__init__()
+        self.conv = ConvNormLayer(
+            ch_in,
+            ch_out,
+            filter_size=1,
+            stride=1,
+            bias_on=False,
+            norm_decay=None)
+        self.residual = residual
+
+    def forward(self, inputs):
+        children = inputs
+        out = self.conv(paddle.concat(inputs, axis=1))
+        if self.residual:
+            out = paddle.add(x=out, y=children[0])
+        out = F.relu(out)
+
+        return out
+
+
+class Tree(nn.Layer):
+    def __init__(self,
+                 level,
+                 block,
+                 ch_in,
+                 ch_out,
+                 stride=1,
+                 level_root=False,
+                 root_dim=0,
+                 root_kernel_size=1,
+                 root_residual=False):
+        super(Tree, self).__init__()
+        if root_dim == 0:
+            root_dim = 2 * ch_out
+        if level_root:
+            root_dim += ch_in
+        if level == 1:
+            self.tree1 = block(ch_in, ch_out, stride)
+            self.tree2 = block(ch_out, ch_out, 1)
+        else:
+            self.tree1 = Tree(
+                level - 1,
+                block,
+                ch_in,
+                ch_out,
+                stride,
+                root_dim=0,
+                root_kernel_size=root_kernel_size,
+                root_residual=root_residual)
+            self.tree2 = Tree(
+                level - 1,
+                block,
+                ch_out,
+                ch_out,
+                1,
+                root_dim=root_dim + ch_out,
+                root_kernel_size=root_kernel_size,
+                root_residual=root_residual)
+
+        if level == 1:
+            self.root = Root(root_dim, ch_out, root_kernel_size, root_residual)
+        self.level_root = level_root
+        self.root_dim = root_dim
+        self.downsample = None
+        self.project = None
+        self.level = level
+        if stride > 1:
+            self.downsample = nn.MaxPool2D(stride, stride=stride)
+        if ch_in != ch_out:
+            self.project = ConvNormLayer(
+                ch_in,
+                ch_out,
+                filter_size=1,
+                stride=1,
+                bias_on=False,
+                norm_decay=None)
+
+    def forward(self, x, residual=None, children=None):
+        children = [] if children is None else children
+        bottom = self.downsample(x) if self.downsample else x
+        residual = self.project(bottom) if self.project else bottom
+        if self.level_root:
+            children.append(bottom)
+        x1 = self.tree1(x, residual)
+        if self.level == 1:
+            x2 = self.tree2(x1)
+            x = self.root([x2, x1] + children)
+        else:
+            children.append(x1)
+            x = self.tree2(x1, children=children)
+        return x
+
+
+@register
+@serializable
+class DLA(nn.Layer):
+    """
+    DLA, see https://arxiv.org/pdf/1707.06484.pdf
+
+    Args:
+        depth (int): DLA depth, should be 34.
+        residual_root (bool): whether use a reidual layer in the root block
+
+    """
+
+    def __init__(self, depth=34, residual_root=False):
+        super(DLA, self).__init__()
+        levels, channels = DLA_cfg[depth]
+        if depth == 34:
+            block = BasicBlock
+        self.channels = channels
+        self.base_layer = nn.Sequential(
+            ConvNormLayer(
+                3,
+                channels[0],
+                filter_size=7,
+                stride=1,
+                bias_on=False,
+                norm_decay=None),
+            nn.ReLU())
+        self.level0 = self._make_conv_level(channels[0], channels[0], levels[0])
+        self.level1 = self._make_conv_level(
+            channels[0], channels[1], levels[1], stride=2)
+        self.level2 = Tree(
+            levels[2],
+            block,
+            channels[1],
+            channels[2],
+            2,
+            level_root=False,
+            root_residual=residual_root)
+        self.level3 = Tree(
+            levels[3],
+            block,
+            channels[2],
+            channels[3],
+            2,
+            level_root=True,
+            root_residual=residual_root)
+        self.level4 = Tree(
+            levels[4],
+            block,
+            channels[3],
+            channels[4],
+            2,
+            level_root=True,
+            root_residual=residual_root)
+        self.level5 = Tree(
+            levels[5],
+            block,
+            channels[4],
+            channels[5],
+            2,
+            level_root=True,
+            root_residual=residual_root)
+
+    def _make_conv_level(self, ch_in, ch_out, conv_num, stride=1):
+        modules = []
+        for i in range(conv_num):
+            modules.extend([
+                ConvNormLayer(
+                    ch_in,
+                    ch_out,
+                    filter_size=3,
+                    stride=stride if i == 0 else 1,
+                    bias_on=False,
+                    norm_decay=None), nn.ReLU()
+            ])
+            ch_in = ch_out
+        return nn.Sequential(*modules)
+
+    @property
+    def out_shape(self):
+        return [ShapeSpec(channels=self.channels[i]) for i in range(6)]
+
+    def forward(self, inputs):
+        outs = []
+        im = inputs['image']
+        feats = self.base_layer(im)
+        for i in range(6):
+            feats = getattr(self, 'level{}'.format(i))(feats)
+            outs.append(feats)
+
+        return outs

ppdet/modeling/heads/__init__.py

@@ -24,6 +24,7 @@ from . import cascade_head
 from . import face_head
 from . import s2anet_head
 from . import keypoint_hrhrnet_head
+from . import centernet_head
 
 from .bbox_head import *
 from .mask_head import *
@@ -37,3 +38,4 @@ from .cascade_head import *
 from .face_head import *
 from .s2anet_head import *
 from .keypoint_hrhrnet_head import *
+from .centernet_head import *

ppdet/modeling/heads/centernet_head.py

+# Copyright (c) 2021 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 math
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle import ParamAttr
+from paddle.nn.initializer import KaimingUniform
+from ppdet.core.workspace import register
+from ppdet.modeling.losses import CTFocalLoss
+
+
+class ConvLayer(nn.Layer):
+    def __init__(self,
+                 ch_in,
+                 ch_out,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 bias=False):
+        super(ConvLayer, self).__init__()
+        bias_attr = False
+        fan_in = ch_in * kernel_size**2
+        bound = 1 / math.sqrt(fan_in)
+        param_attr = paddle.ParamAttr(initializer=KaimingUniform())
+        if bias:
+            bias_attr = paddle.ParamAttr(
+                initializer=nn.initializer.Uniform(-bound, bound))
+        self.conv = nn.Conv2D(
+            in_channels=ch_in,
+            out_channels=ch_out,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            weight_attr=param_attr,
+            bias_attr=bias_attr)
+
+    def forward(self, inputs):
+        out = self.conv(inputs)
+
+        return out
+
+
+@register
+class CenterNetHead(nn.Layer):
+    """
+    Args:
+        in_channels (int): the channel number of input to CenterNetHead.
+        num_classes (int): the number of classes, 80 by default.
+        head_planes (int): the channel number in all head, 256 by default.
+        heatmap_weight (float): the weight of heatmap loss, 1 by default.
+        regress_ltrb (bool): whether to regress left/top/right/bottom or
+            width/height for a box, true by default
+        size_weight (float): the weight of box size loss, 0.1 by default.
+        offset_weight (float): the weight of center offset loss, 1 by default.
+
+    """
+
+    __shared__ = ['num_classes']
+
+    def __init__(self,
+                 in_channels,
+                 num_classes=80,
+                 head_planes=256,
+                 heatmap_weight=1,
+                 regress_ltrb=True,
+                 size_weight=0.1,
+                 offset_weight=1):
+        super(CenterNetHead, self).__init__()
+        self.weights = {
+            'heatmap': heatmap_weight,
+            'size': size_weight,
+            'offset': offset_weight
+        }
+        self.heatmap = nn.Sequential(
+            ConvLayer(
+                in_channels, head_planes, kernel_size=3, padding=1, bias=True),
+            nn.ReLU(),
+            ConvLayer(
+                head_planes,
+                num_classes,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=True))
+        self.heatmap[2].conv.bias[:] = -2.19
+        self.size = nn.Sequential(
+            ConvLayer(
+                in_channels, head_planes, kernel_size=3, padding=1, bias=True),
+            nn.ReLU(),
+            ConvLayer(
+                head_planes,
+                4 if regress_ltrb else 2,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=True))
+        self.offset = nn.Sequential(
+            ConvLayer(
+                in_channels, head_planes, kernel_size=3, padding=1, bias=True),
+            nn.ReLU(),
+            ConvLayer(
+                head_planes, 2, kernel_size=1, stride=1, padding=0, bias=True))
+        self.focal_loss = CTFocalLoss()
+
+    @classmethod
+    def from_config(cls, cfg, input_shape):
+        if isinstance(input_shape, (list, tuple)):
+            input_shape = input_shape[0]
+        return {'in_channels': input_shape.channels}
+
+    def forward(self, feat, inputs):
+        heatmap = self.heatmap(feat)
+        size = self.size(feat)
+        offset = self.offset(feat)
+        if self.training:
+            loss = self.get_loss(heatmap, size, offset, self.weights, inputs)
+            return loss
+        else:
+            heatmap = F.sigmoid(heatmap)
+            return {'heatmap': heatmap, 'size': size, 'offset': offset}
+
+    def get_loss(self, heatmap, size, offset, weights, inputs):
+        heatmap_target = inputs['heatmap']
+        size_target = inputs['size']
+        offset_target = inputs['offset']
+        index = inputs['index']
+        mask = inputs['index_mask']
+        heatmap = paddle.clip(F.sigmoid(heatmap), 1e-4, 1 - 1e-4)
+        heatmap_loss = self.focal_loss(heatmap, heatmap_target)
+
+        size = paddle.transpose(size, perm=[0, 2, 3, 1])
+        size_n, size_h, size_w, size_c = size.shape
+        size = paddle.reshape(size, shape=[size_n, -1, size_c])
+        index = paddle.unsqueeze(index, 2)
+        batch_inds = list()
+        for i in range(size_n):
+            batch_ind = paddle.full(
+                shape=[1, index.shape[1], 1], fill_value=i, dtype='int64')
+            batch_inds.append(batch_ind)
+        batch_inds = paddle.concat(batch_inds, axis=0)
+        index = paddle.concat(x=[batch_inds, index], axis=2)
+        pos_size = paddle.gather_nd(size, index=index)
+        mask = paddle.unsqueeze(mask, axis=2)
+        size_mask = paddle.expand_as(mask, pos_size)
+        size_mask = paddle.cast(size_mask, dtype=pos_size.dtype)
+        pos_num = size_mask.sum()
+        size_mask.stop_gradient = True
+        size_target.stop_gradient = True
+        size_loss = F.l1_loss(
+            pos_size * size_mask, size_target * size_mask, reduction='sum')
+        size_loss = size_loss / (pos_num + 1e-4)
+
+        offset = paddle.transpose(offset, perm=[0, 2, 3, 1])
+        offset_n, offset_h, offset_w, offset_c = offset.shape
+        offset = paddle.reshape(offset, shape=[offset_n, -1, offset_c])
+        pos_offset = paddle.gather_nd(offset, index=index)
+        offset_mask = paddle.expand_as(mask, pos_offset)
+        offset_mask = paddle.cast(offset_mask, dtype=pos_offset.dtype)
+        pos_num = offset_mask.sum()
+        offset_mask.stop_gradient = True
+        offset_target.stop_gradient = True
+        offset_loss = F.l1_loss(
+            pos_offset * offset_mask,
+            offset_target * offset_mask,
+            reduction='sum')
+        offset_loss = offset_loss / (pos_num + 1e-4)
+
+        det_loss = weights['heatmap'] * heatmap_loss + weights[
+            'size'] * size_loss + weights['offset'] * offset_loss
+
+        return {
+            'det_loss': det_loss,
+            'heatmap_loss': heatmap_loss,
+            'size_loss': size_loss,
+            'offset_loss': offset_loss
+        }

ppdet/modeling/layers.py

@@ -52,7 +52,9 @@ class DeformableConvV2(nn.Layer):
                  bias_attr=None,
                  lr_scale=1,
                  regularizer=None,
-                 skip_quant=False):
+                 skip_quant=False,
+                 dcn_bias_regularizer=L2Decay(0.),
+                 dcn_bias_lr_scale=2.):
         super(DeformableConvV2, self).__init__()
         self.offset_channel = 2 * kernel_size**2
         self.mask_channel = kernel_size**2
@@ -79,8 +81,8 @@ class DeformableConvV2(nn.Layer):
             # in FCOS-DCN head, specifically need learning_rate and regularizer
             dcn_bias_attr = ParamAttr(
                 initializer=Constant(value=0),
-                regularizer=L2Decay(0.),
-                learning_rate=2.)
+                regularizer=dcn_bias_regularizer,
+                learning_rate=dcn_bias_lr_scale)
         else:
             # in ResNet backbone, do not need bias
             dcn_bias_attr = False
@@ -122,7 +124,9 @@ class ConvNormLayer(nn.Layer):
                  freeze_norm=False,
                  initializer=Normal(
                      mean=0., std=0.01),
-                 skip_quant=False):
+                 skip_quant=False,
+                 dcn_lr_scale=2.,
+                 dcn_regularizer=L2Decay(0.)):
         super(ConvNormLayer, self).__init__()
         assert norm_type in ['bn', 'sync_bn', 'gn']
 
@@ -157,15 +161,17 @@ class ConvNormLayer(nn.Layer):
                 weight_attr=ParamAttr(
                     initializer=initializer, learning_rate=1.),
                 bias_attr=True,
-                lr_scale=2.,
-                regularizer=L2Decay(norm_decay),
+                lr_scale=dcn_lr_scale,
+                regularizer=dcn_regularizer,
                 skip_quant=skip_quant)
 
         norm_lr = 0. if freeze_norm else 1.
         param_attr = ParamAttr(
-            learning_rate=norm_lr, regularizer=L2Decay(norm_decay))
+            learning_rate=norm_lr,
+            regularizer=L2Decay(norm_decay) if norm_decay is not None else None)
         bias_attr = ParamAttr(
-            learning_rate=norm_lr, regularizer=L2Decay(norm_decay))
+            learning_rate=norm_lr,
+            regularizer=L2Decay(norm_decay) if norm_decay is not None else None)
         if norm_type == 'bn':
             self.norm = nn.BatchNorm2D(
                 ch_out, weight_attr=param_attr, bias_attr=bias_attr)

ppdet/modeling/losses/__init__.py

@@ -21,6 +21,7 @@ from . import solov2_loss
 from . import ctfocal_loss
 from . import keypoint_loss
 from . import jde_loss
+from . import fairmot_loss
 
 from .yolo_loss import *
 from .iou_aware_loss import *
@@ -31,3 +32,4 @@ from .solov2_loss import *
 from .ctfocal_loss import *
 from .keypoint_loss import *
 from .jde_loss import *
+from .fairmot_loss import *

ppdet/modeling/losses/fairmot_loss.py

+# Copyright (c) 2021 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+import paddle.nn as nn
+from paddle.nn.initializer import Constant
+from ppdet.core.workspace import register
+
+__all__ = ['FairMOTLoss']
+
+
+@register
+class FairMOTLoss(nn.Layer):
+    def __init__(self):
+        super(FairMOTLoss, self).__init__()
+        self.det_weight = self.create_parameter(
+            shape=[1], default_initializer=Constant(-1.85))
+        self.reid_weight = self.create_parameter(
+            shape=[1], default_initializer=Constant(-1.05))
+
+    def forward(self, det_loss, reid_loss):
+        loss = paddle.exp(-self.det_weight) * det_loss + paddle.exp(
+            -self.reid_weight) * reid_loss + (self.det_weight + self.reid_weight
+                                              )
+        loss *= 0.5
+        return {'loss': loss}

ppdet/modeling/mot/tracker/jde_tracker.py

@@ -46,6 +46,9 @@ class JDETracker(object):
         unconfirmed_thresh (float): linear assignment threshold of 
             unconfirmed stracks and unmatched detections
         motion (object): KalmanFilter instance
+        conf_thres (float): confidence threshold for tracking
+        metric_type (str): either "euclidean" or "cosine", the distance metric 
+            used for measurement to track association.
     """
 
     def __init__(self,
@@ -55,7 +58,9 @@ class JDETracker(object):
                  tracked_thresh=0.7,
                  r_tracked_thresh=0.5,
                  unconfirmed_thresh=0.7,
-                 motion='KalmanFilter'):
+                 motion='KalmanFilter',
+                 conf_thres=0,
+                 metric_type='euclidean'):
         self.det_thresh = det_thresh
         self.track_buffer = track_buffer
         self.min_box_area = min_box_area
@@ -63,6 +68,8 @@ class JDETracker(object):
         self.r_tracked_thresh = r_tracked_thresh
         self.unconfirmed_thresh = unconfirmed_thresh
         self.motion = motion
+        self.conf_thres = conf_thres
+        self.metric_type = metric_type
 
         self.frame_id = 0
         self.tracked_stracks = []
@@ -96,6 +103,14 @@ class JDETracker(object):
         # removed. (Lost for some time lesser than the threshold for removing)
         removed_stracks = []
 
+        remain_inds = paddle.nonzero(pred_dets[:, 4] > self.conf_thres)
+        if remain_inds.shape[0] == 0:
+            pred_dets = paddle.zeros([0, 1])
+            pred_embs = paddle.zeros([0, 1])
+        else:
+            pred_dets = paddle.gather(pred_dets, remain_inds)
+            pred_embs = paddle.gather(pred_embs, remain_inds)
+
         # Filter out the image with box_num = 0. pred_dets = [[0.0, 0.0, 0.0 ,0.0]]
         empty_pred = True if len(pred_dets) == 1 and paddle.sum(
             pred_dets) == 0.0 else False
@@ -125,7 +140,8 @@ class JDETracker(object):
         # Predict the current location with KF
         STrack.multi_predict(strack_pool, self.motion)
 
-        dists = matching.embedding_distance(strack_pool, detections)
+        dists = matching.embedding_distance(
+            strack_pool, detections, metric=self.metric_type)
         dists = matching.fuse_motion(self.motion, dists, strack_pool,
                                      detections)
         # The dists is the list of distances of the detection with the tracks in strack_pool

ppdet/modeling/necks/__init__.py

@@ -16,8 +16,10 @@ from . import fpn
 from . import yolo_fpn
 from . import hrfpn
 from . import ttf_fpn
+from . import centernet_fpn
 
 from .fpn import *
 from .yolo_fpn import *
 from .hrfpn import *
 from .ttf_fpn import *
+from .centernet_fpn import *

ppdet/modeling/necks/centernet_fpn.py

+# Copyright (c) 2021 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 math
+import paddle
+import paddle.nn.functional as F
+from paddle import ParamAttr
+import paddle.nn as nn
+from paddle.nn.initializer import KaimingUniform
+from ppdet.core.workspace import register, serializable
+from ppdet.modeling.layers import ConvNormLayer
+from ..shape_spec import ShapeSpec
+
+
+def fill_up_weights(up):
+    weight = up.weight
+    f = math.ceil(weight.shape[2] / 2)
+    c = (2 * f - 1 - f % 2) / (2. * f)
+    for i in range(weight.shape[2]):
+        for j in range(weight.shape[3]):
+            weight[0, 0, i, j] = \
+                (1 - math.fabs(i / f - c)) * (1 - math.fabs(j / f - c))
+    for c in range(1, weight.shape[0]):
+        weight[c, 0, :, :] = weight[0, 0, :, :]
+
+
+class IDAUp(nn.Layer):
+    def __init__(self, ch_ins, ch_out, up_strides, dcn_v2=True):
+        super(IDAUp, self).__init__()
+        for i in range(1, len(ch_ins)):
+            ch_in = ch_ins[i]
+            up_s = int(up_strides[i])
+            proj = nn.Sequential(
+                ConvNormLayer(
+                    ch_in,
+                    ch_out,
+                    filter_size=3,
+                    stride=1,
+                    use_dcn=dcn_v2,
+                    bias_on=dcn_v2,
+                    norm_decay=None,
+                    dcn_lr_scale=1.,
+                    dcn_regularizer=None),
+                nn.ReLU())
+            node = nn.Sequential(
+                ConvNormLayer(
+                    ch_out,
+                    ch_out,
+                    filter_size=3,
+                    stride=1,
+                    use_dcn=dcn_v2,
+                    bias_on=dcn_v2,
+                    norm_decay=None,
+                    dcn_lr_scale=1.,
+                    dcn_regularizer=None),
+                nn.ReLU())
+
+            param_attr = paddle.ParamAttr(initializer=KaimingUniform())
+            up = nn.Conv2DTranspose(
+                ch_out,
+                ch_out,
+                kernel_size=up_s * 2,
+                weight_attr=param_attr,
+                stride=up_s,
+                padding=up_s // 2,
+                groups=ch_out,
+                bias_attr=False)
+            # TODO: uncomment fill_up_weights
+            #fill_up_weights(up)
+            setattr(self, 'proj_' + str(i), proj)
+            setattr(self, 'up_' + str(i), up)
+            setattr(self, 'node_' + str(i), node)
+
+    def forward(self, inputs, start_level, end_level):
+        for i in range(start_level + 1, end_level):
+            upsample = getattr(self, 'up_' + str(i - start_level))
+            project = getattr(self, 'proj_' + str(i - start_level))
+
+            inputs[i] = project(inputs[i])
+            inputs[i] = upsample(inputs[i])
+            node = getattr(self, 'node_' + str(i - start_level))
+            inputs[i] = node(paddle.add(inputs[i], inputs[i - 1]))
+
+
+class DLAUp(nn.Layer):
+    def __init__(self, start_level, channels, scales, ch_in=None, dcn_v2=True):
+        super(DLAUp, self).__init__()
+        self.start_level = start_level
+        if ch_in is None:
+            ch_in = channels
+        self.channels = channels
+        channels = list(channels)
+        scales = np.array(scales, dtype=int)
+        for i in range(len(channels) - 1):
+            j = -i - 2
+            setattr(
+                self,
+                'ida_{}'.format(i),
+                IDAUp(
+                    ch_in[j:],
+                    channels[j],
+                    scales[j:] // scales[j],
+                    dcn_v2=dcn_v2))
+            scales[j + 1:] = scales[j]
+            ch_in[j + 1:] = [channels[j] for _ in channels[j + 1:]]
+
+    def forward(self, inputs):
+        out = [inputs[-1]]  # start with 32
+        for i in range(len(inputs) - self.start_level - 1):
+            ida = getattr(self, 'ida_{}'.format(i))
+            ida(inputs, len(inputs) - i - 2, len(inputs))
+            out.insert(0, inputs[-1])
+        return out
+
+
+@register
+@serializable
+class CenterNetDLAFPN(nn.Layer):
+    """
+    Args:
+        in_channels (list): number of input feature channels from backbone.
+            [16, 32, 64, 128, 256, 512] by default, means the channels of DLA-34
+        down_ratio (int): the down ratio from images to heatmap, 4 by default
+        last_level (int): the last level of input feature fed into the upsamplng block
+        out_channel (int): the channel of the output feature, 0 by default means
+            the channel of the input feature whose down ratio is `down_ratio`
+        dcn_v2 (bool): whether use the DCNv2, true by default
+        
+    """
+
+    def __init__(self,
+                 in_channels,
+                 down_ratio=4,
+                 last_level=5,
+                 out_channel=0,
+                 dcn_v2=True):
+        super(CenterNetDLAFPN, self).__init__()
+        self.first_level = int(np.log2(down_ratio))
+        self.down_ratio = down_ratio
+        self.last_level = last_level
+        scales = [2**i for i in range(len(in_channels[self.first_level:]))]
+        self.dla_up = DLAUp(
+            self.first_level,
+            in_channels[self.first_level:],
+            scales,
+            dcn_v2=dcn_v2)
+        self.out_channel = out_channel
+        if out_channel == 0:
+            self.out_channel = in_channels[self.first_level]
+        self.ida_up = IDAUp(
+            in_channels[self.first_level:self.last_level],
+            self.out_channel,
+            [2**i for i in range(self.last_level - self.first_level)],
+            dcn_v2=dcn_v2)
+
+    @classmethod
+    def from_config(cls, cfg, input_shape):
+        return {'in_channels': [i.channels for i in input_shape]}
+
+    def forward(self, body_feats):
+        dla_up_feats = self.dla_up(body_feats)
+
+        ida_up_feats = []
+        for i in range(self.last_level - self.first_level):
+            ida_up_feats.append(dla_up_feats[i].clone())
+
+        self.ida_up(ida_up_feats, 0, len(ida_up_feats))
+
+        return ida_up_feats[-1]
+
+    @property
+    def out_shape(self):
+        return [ShapeSpec(channels=self.out_channel, stride=self.down_ratio)]

ppdet/modeling/post_process.py

@@ -18,6 +18,7 @@ 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, rbox2poly
+from ppdet.modeling.layers import TTFBox
 try:
     from collections.abc import Sequence
 except Exception:
@@ -29,6 +30,7 @@ __all__ = [
     'FCOSPostProcess',
     'S2ANetBBoxPostProcess',
     'JDEBBoxPostProcess',
+    'CenterNetPostProcess',
 ]
 
 
@@ -352,3 +354,95 @@ class JDEBBoxPostProcess(BBoxPostProcess):
             nms_keep_idx = paddle.to_tensor(np.array([[0]], dtype='int32'))
 
         return boxes_idx, bbox_pred, bbox_num, nms_keep_idx
+
+
+@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']
+
+    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, clses, ys, xs = self._topk(heat)
+        scores = paddle.tensor.unsqueeze(scores, [1])
+        clses = paddle.tensor.unsqueeze(clses, [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, paddle.shape(reg_t)[-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, paddle.shape(wh_t)[-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 = paddle.shape(hm)
+        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 = paddle.shape(bboxes)
+        boxes_shape.stop_gradient = True
+        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
+        else:
+            results = paddle.concat([clses, scores, bboxes], axis=1)
+            return results, paddle.shape(results)[0:1]

ppdet/modeling/reid/__init__.py

@@ -15,7 +15,9 @@
 from . import jde_embedding_head
 from . import pyramidal_embedding
 from . import resnet
+from . import fairmot_embedding_head
 
 from .jde_embedding_head import *
 from .pyramidal_embedding import *
 from .resnet import *
+from .fairmot_embedding_head import *

ppdet/modeling/reid/fairmot_embedding_head.py

+# Copyright (c) 2021 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 math
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle import ParamAttr
+from paddle.nn.initializer import KaimingUniform, Uniform
+from ppdet.core.workspace import register
+from ppdet.modeling.heads.centernet_head import ConvLayer
+
+__all__ = ['FairMOTEmbeddingHead']
+
+
+@register
+class FairMOTEmbeddingHead(nn.Layer):
+    """
+    Args:
+        in_channels (int): the channel number of input to FairMOTEmbeddingHead.
+        ch_head (int): the channel of features before fed into embedding, 256 by default.
+        ch_emb (int): the channel of the embedding feature, 128 by default.
+        num_identifiers (int): the number of identifiers, 14455 by default.
+
+    """
+
+    def __init__(self,
+                 in_channels,
+                 ch_head=256,
+                 ch_emb=128,
+                 num_identifiers=14455):
+        super(FairMOTEmbeddingHead, self).__init__()
+        self.reid = nn.Sequential(
+            ConvLayer(
+                in_channels, ch_head, kernel_size=3, padding=1, bias=True),
+            nn.ReLU(),
+            ConvLayer(
+                ch_head, ch_emb, kernel_size=1, stride=1, padding=0, bias=True))
+        param_attr = paddle.ParamAttr(initializer=KaimingUniform())
+        bound = 1 / math.sqrt(ch_emb)
+        bias_attr = paddle.ParamAttr(initializer=Uniform(-bound, bound))
+        self.classifier = nn.Linear(
+            ch_emb,
+            num_identifiers,
+            weight_attr=param_attr,
+            bias_attr=bias_attr)
+        self.reid_loss = nn.CrossEntropyLoss(ignore_index=-1, reduction='sum')
+        # When num_identifiers is 1, emb_scale is set as 1
+        self.emb_scale = math.sqrt(2) * math.log(
+            num_identifiers - 1) if num_identifiers > 1 else 1
+
+    @classmethod
+    def from_config(cls, cfg, input_shape):
+        if isinstance(input_shape, (list, tuple)):
+            input_shape = input_shape[0]
+        return {'in_channels': input_shape.channels}
+
+    def forward(self, feat, inputs):
+        reid_feat = self.reid(feat)
+        if self.training:
+            loss = self.get_loss(reid_feat, inputs)
+            return loss
+        else:
+            reid_feat = F.normalize(reid_feat)
+            return reid_feat
+
+    def get_loss(self, feat, inputs):
+        index = inputs['index']
+        mask = inputs['index_mask']
+        target = inputs['reid']
+        target = paddle.masked_select(target, mask > 0)
+        target = paddle.unsqueeze(target, 1)
+
+        feat = paddle.transpose(feat, perm=[0, 2, 3, 1])
+        feat_n, feat_h, feat_w, feat_c = feat.shape
+        feat = paddle.reshape(feat, shape=[feat_n, -1, feat_c])
+        index = paddle.unsqueeze(index, 2)
+        batch_inds = list()
+        for i in range(feat_n):
+            batch_ind = paddle.full(
+                shape=[1, index.shape[1], 1], fill_value=i, dtype='int64')
+            batch_inds.append(batch_ind)
+        batch_inds = paddle.concat(batch_inds, axis=0)
+        index = paddle.concat(x=[batch_inds, index], axis=2)
+        feat = paddle.gather_nd(feat, index=index)
+
+        mask = paddle.unsqueeze(mask, axis=2)
+        mask = paddle.expand_as(mask, feat)
+        mask.stop_gradient = True
+        feat = paddle.masked_select(feat, mask > 0)
+        feat = paddle.reshape(feat, shape=[-1, feat_c])
+        feat = F.normalize(feat)
+        feat = self.emb_scale * feat
+        logit = self.classifier(feat)
+        target.stop_gradient = True
+        loss = self.reid_loss(logit, target)
+        valid = (target != self.reid_loss.ignore_index)
+        valid.stop_gradient = True
+        count = paddle.sum((paddle.cast(valid, dtype=np.int32)))
+        count.stop_gradient = True
+        if count > 0:
+            loss = loss / count
+
+        return loss