[cherry-pick]cherry-pick of petr and tinypose3d_human3.6m (#7816)

* keypoint petr (#7774)

* petr train ok

train ok

refix augsize

affine size fix

update msdeformable

fix flip/affine

fix clip

add resize area

add distortion

debug mode

fix pos_inds

update edge joints

update word mistake

* delete extra codes;adapt transformer modify;update code format

* reverse old transformer modify

* integrate datasets

* add config and architecture for human36m (#7802)

* add config and architecture for human36m

* modify TinyPose3DHRNet to support human3.6M dataset

* delete useless class

---------
Co-authored-by: XYZ <1290573099@qq.com>

.gitignore

@@ -18,9 +18,9 @@ __pycache__/
 
 # Distribution / packaging
 /bin/
-/build/
+*build/
 /develop-eggs/
-/dist/
+*dist/
 /eggs/
 /lib/
 /lib64/
@@ -30,7 +30,7 @@ __pycache__/
 /parts/
 /sdist/
 /var/
-/*.egg-info/
+*.egg-info/
 /.installed.cfg
 /*.egg
 /.eggs

configs/keypoint/README.md

@@ -56,8 +56,10 @@ PaddleDetection 中的关键点检测部分紧跟最先进的算法，包括 Top
 ## 模型库
 
 COCO数据集
+
 | 模型              |  方案              |输入尺寸 | AP(coco val) |                           模型下载                           | 配置文件 |  
 | :---------------- | -------- | :----------: | :----------------------------------------------------------: | ----------------------------------------------------| ------- |
+| PETR_Res50       |One-Stage| 512      |     65.5     | [petr_res50.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/petr_resnet50_16x2_coco.pdparams) | [config](./petr/petr_resnet50_16x2_coco.yml)       |
 | HigherHRNet-w32       |Bottom-Up| 512      |     67.1     | [higherhrnet_hrnet_w32_512.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_512.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_512.yml)       |
 | HigherHRNet-w32       | Bottom-Up| 640      |     68.3     | [higherhrnet_hrnet_w32_640.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_640.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_640.yml)       |
 | HigherHRNet-w32+SWAHR |Bottom-Up|  512      |     68.9     | [higherhrnet_hrnet_w32_512_swahr.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_512_swahr.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_512_swahr.yml) |

configs/keypoint/README_en.md

@@ -62,6 +62,7 @@ At the same time, PaddleDetection provides a self-developed real-time keypoint d
 COCO Dataset
 | Model              | Input Size | AP(coco val) |                           Model Download                           | Config File                                                    |
 | :---------------- | -------- | :----------: | :----------------------------------------------------------: | ----------------------------------------------------------- |
+| PETR_Res50       |One-Stage| 512      |     65.5     | [petr_res50.pdparams](https://bj.bcebos.com/v1/paddledet/models/keypoint/petr_resnet50_16x2_coco.pdparams) | [config](./petr/petr_resnet50_16x2_coco.yml)       |
 | HigherHRNet-w32       | 512      |     67.1     | [higherhrnet_hrnet_w32_512.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_512.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_512.yml)       |
 | HigherHRNet-w32       | 640      |     68.3     | [higherhrnet_hrnet_w32_640.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_640.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_640.yml)       |
 | HigherHRNet-w32+SWAHR | 512      |     68.9     | [higherhrnet_hrnet_w32_512_swahr.pdparams](https://paddledet.bj.bcebos.com/models/keypoint/higherhrnet_hrnet_w32_512_swahr.pdparams) | [config](./higherhrnet/higherhrnet_hrnet_w32_512_swahr.yml) |

configs/keypoint/petr/petr_resnet50_16x2_coco.yml

+use_gpu: true
+log_iter: 50
+save_dir: output
+snapshot_epoch: 1
+weights: output/petr_resnet50_16x2_coco/model_final
+epoch: 100
+num_joints: &num_joints 17
+pixel_std: &pixel_std 200
+metric: COCO
+num_classes: 1
+trainsize: &trainsize 512
+flip_perm: &flip_perm [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
+find_unused_parameters: False
+
+#####model
+architecture: PETR
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/PETR_pretrained.pdparams
+
+PETR:
+  backbone:
+    name: ResNet
+    depth: 50
+    variant: b
+    norm_type: bn
+    freeze_norm: True
+    freeze_at: 0
+    return_idx: [1,2,3]
+    num_stages: 4
+    lr_mult_list: [0.1, 0.1, 0.1, 0.1]
+  neck:
+    name: ChannelMapper
+    in_channels: [512, 1024, 2048]
+    kernel_size: 1
+    out_channels: 256
+    norm_type: "gn"
+    norm_groups: 32
+    act: None
+    num_outs: 4
+  bbox_head:
+    name: PETRHead
+    num_query: 300
+    num_classes: 1  # only person
+    in_channels: 2048
+    sync_cls_avg_factor: true
+    with_kpt_refine: true
+    transformer:
+      name: PETRTransformer
+      as_two_stage: true
+      encoder:
+        name: TransformerEncoder
+        encoder_layer:
+          name: TransformerEncoderLayer
+          d_model: 256
+          attn:
+            name: MSDeformableAttention
+            embed_dim: 256
+            num_heads: 8
+            num_levels: 4
+            num_points: 4
+          dim_feedforward: 1024
+          dropout: 0.1
+        num_layers: 6
+      decoder:
+        name: PETR_TransformerDecoder
+        num_layers: 3
+        return_intermediate: true
+        decoder_layer:
+          name: PETR_TransformerDecoderLayer
+          d_model: 256
+          dim_feedforward: 1024
+          dropout: 0.1
+          self_attn:
+            name: MultiHeadAttention
+            embed_dim: 256
+            num_heads: 8
+            dropout: 0.1
+          cross_attn:
+            name: MultiScaleDeformablePoseAttention
+            embed_dims: 256
+            num_heads: 8
+            num_levels: 4
+            num_points: 17
+      hm_encoder:
+        name: TransformerEncoder
+        encoder_layer:
+          name: TransformerEncoderLayer
+          d_model: 256
+          attn:
+            name: MSDeformableAttention
+            embed_dim: 256
+            num_heads: 8
+            num_levels: 1
+            num_points: 4
+          dim_feedforward: 1024
+          dropout: 0.1
+        num_layers: 1
+      refine_decoder:
+        name: PETR_DeformableDetrTransformerDecoder
+        num_layers: 2
+        return_intermediate: true
+        decoder_layer:
+          name: PETR_TransformerDecoderLayer
+          d_model: 256
+          dim_feedforward: 1024
+          dropout: 0.1
+          self_attn:
+            name: MultiHeadAttention
+            embed_dim: 256
+            num_heads: 8
+            dropout: 0.1
+          cross_attn:
+            name: MSDeformableAttention
+            embed_dim: 256
+            num_levels: 4
+    positional_encoding:
+      name: PositionEmbedding
+      num_pos_feats: 128
+      normalize: true
+      offset: -0.5
+    loss_cls:
+        name: Weighted_FocalLoss
+        use_sigmoid: true
+        gamma: 2.0
+        alpha: 0.25
+        loss_weight: 2.0
+        reduction: "mean"
+    loss_kpt:
+      name: L1Loss
+      loss_weight: 70.0
+    loss_kpt_rpn:
+      name: L1Loss
+      loss_weight: 70.0
+    loss_oks:
+      name: OKSLoss
+      loss_weight: 2.0
+    loss_hm:
+      name: CenterFocalLoss
+      loss_weight: 4.0
+    loss_kpt_refine:
+      name: L1Loss
+      loss_weight: 80.0
+    loss_oks_refine:
+      name: OKSLoss
+      loss_weight: 3.0
+    assigner:
+        name: PoseHungarianAssigner
+        cls_cost:
+          name: FocalLossCost
+          weight: 2.0
+        kpt_cost:
+          name: KptL1Cost
+          weight: 70.0
+        oks_cost:
+          name: OksCost
+          weight: 7.0
+
+#####optimizer
+LearningRate:
+  base_lr: 0.0002
+  schedulers:
+  - !PiecewiseDecay
+    milestones: [80]
+    gamma: 0.1
+    use_warmup: false
+  # - !LinearWarmup
+  #   start_factor: 0.001
+  #   steps: 1000
+
+OptimizerBuilder:
+  clip_grad_by_norm: 0.1
+  optimizer:
+    type: AdamW
+  regularizer:
+    factor: 0.0001
+    type: L2
+
+
+#####data
+TrainDataset:
+  !KeypointBottomUpCocoDataset
+    image_dir: train2017
+    anno_path: annotations/person_keypoints_train2017.json
+    dataset_dir: dataset/coco
+    num_joints: *num_joints
+    return_mask: false
+
+EvalDataset:
+  !KeypointBottomUpCocoDataset
+    image_dir: val2017
+    anno_path: annotations/person_keypoints_val2017.json
+    dataset_dir: dataset/coco
+    num_joints: *num_joints
+    test_mode: true
+    return_mask: false
+
+TestDataset:
+  !ImageFolder
+    anno_path: dataset/coco/keypoint_imagelist.txt
+
+worker_num: 2
+global_mean: &global_mean [0.485, 0.456, 0.406]
+global_std: &global_std [0.229, 0.224, 0.225]
+TrainReader:
+  sample_transforms:
+    - Decode: {}
+    - PhotoMetricDistortion:
+        brightness_delta: 32
+        contrast_range: [0.5, 1.5]
+        saturation_range: [0.5, 1.5]
+        hue_delta: 18
+    - KeyPointFlip:
+        flip_prob: 0.5
+        flip_permutation: *flip_perm
+    - RandomAffine:
+        max_degree: 30
+        scale: [1.0, 1.0]
+        max_shift: 0.
+        trainsize: -1
+    - RandomSelect: { transforms1: [ RandomShortSideRangeResize: { scales: [[400, 1400], [1400, 1400]]} ],
+                      transforms2: [
+                          RandomShortSideResize: { short_side_sizes: [ 400, 500, 600 ] },
+                          RandomSizeCrop: { min_size: 384, max_size: 600},
+                          RandomShortSideRangeResize: { scales: [[400, 1400], [1400, 1400]]} ]}
+  batch_transforms:
+    - NormalizeImage: {mean: *global_mean, std: *global_std, is_scale: True}
+    - PadGT: {pad_img: True, minimum_gtnum: 1}
+    - Permute: {}
+  batch_size: 2
+  shuffle: true
+  drop_last: true
+  use_shared_memory: true
+  collate_batch: true
+
+EvalReader:
+  sample_transforms:
+    - PETR_Resize: {img_scale: [[800, 1333]], keep_ratio: True}
+    # - MultiscaleTestResize: {origin_target_size: [[800, 1333]], use_flip: false}
+    - NormalizeImage:
+        mean: *global_mean
+        std: *global_std
+        is_scale: true
+    - Permute: {}
+  batch_size: 1
+
+TestReader:
+  sample_transforms:
+    - Decode: {}
+    - EvalAffine:
+        size: *trainsize
+    - NormalizeImage:
+        mean: *global_mean
+        std: *global_std
+        is_scale: true
+    - Permute: {}
+  batch_size: 1

configs/pose3d/tinypose3d_human36M.yml

+use_gpu: true
+log_iter: 5
+save_dir: output
+snapshot_epoch: 1
+weights: output/tinypose3d_human36M/model_final
+epoch: 220
+num_joints: &num_joints 24
+pixel_std: &pixel_std 200
+metric: Pose3DEval
+num_classes: 1
+train_height: &train_height 128
+train_width: &train_width 128
+trainsize: &trainsize [*train_width, *train_height]
+
+#####model
+architecture: TinyPose3DHRNet
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams
+
+TinyPose3DHRNet:
+  backbone: LiteHRNet
+  post_process: HR3DNetPostProcess
+  fc_channel: 1024
+  num_joints: *num_joints
+  width: &width 40
+  loss: Pose3DLoss
+
+LiteHRNet:
+  network_type: wider_naive
+  freeze_at: -1
+  freeze_norm: false
+  return_idx: [0]
+
+Pose3DLoss:
+  weight_3d: 1.0
+  weight_2d: 0.0
+
+#####optimizer
+LearningRate:
+  base_lr: 0.0001
+  schedulers:
+  - !PiecewiseDecay
+    milestones: [17, 21]
+    gamma: 0.1
+  - !LinearWarmup
+    start_factor: 0.01
+    steps: 1000
+
+OptimizerBuilder:
+  optimizer:
+    type: Adam
+  regularizer:
+    factor: 0.0
+    type: L2
+
+
+#####data
+TrainDataset:
+  !Pose3DDataset
+    dataset_dir: Human3.6M
+    image_dirs: ["Images"]
+    anno_list:  ['Human3.6m_train.json']
+    num_joints: *num_joints
+    test_mode: False
+
+EvalDataset:
+  !Pose3DDataset
+    dataset_dir: Human3.6M
+    image_dirs: ["Images"]
+    anno_list: ['Human3.6m_valid.json']
+    num_joints: *num_joints
+    test_mode: True
+
+TestDataset:
+  !ImageFolder
+    anno_path: dataset/coco/keypoint_imagelist.txt
+
+worker_num: 4
+global_mean: &global_mean [0.485, 0.456, 0.406]
+global_std: &global_std [0.229, 0.224, 0.225]
+TrainReader:
+  sample_transforms:
+    - SinglePoseAffine:
+        trainsize: *trainsize
+        rotate: [0.5, 30] #[prob, rotate range]
+        scale: [0.5, 0.25] #[prob, scale range]
+  batch_transforms:
+    - NormalizeImage:
+        mean: *global_mean
+        std: *global_std
+        is_scale: true
+    - Permute: {}
+  batch_size: 128
+  shuffle: true
+  drop_last: true
+
+EvalReader:
+  sample_transforms:
+    - SinglePoseAffine:
+        trainsize: *trainsize
+        rotate: [0., 30]
+        scale: [0., 0.25]
+  batch_transforms:
+    - NormalizeImage:
+        mean: *global_mean
+        std: *global_std
+        is_scale: true
+    - Permute: {}
+  batch_size: 128
+
+TestReader:
+  inputs_def:
+    image_shape: [3, *train_height, *train_width]
+  sample_transforms:
+    - Decode: {}
+    - TopDownEvalAffine:
+        trainsize: *trainsize
+    - NormalizeImage:
+        mean: *global_mean
+        std: *global_std
+        is_scale: true
+    - Permute: {}
+  batch_size: 1
+  fuse_normalize: false

docs/tutorials/data/PrepareKeypointDataSet.md

@@ -82,7 +82,7 @@ MPII keypoint indexes:
 ```
 {
     'joints_vis': [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
-    'joints': [
+    'gt_joints': [
         [-1.0, -1.0],
         [-1.0, -1.0],
         [-1.0, -1.0],

docs/tutorials/data/PrepareKeypointDataSet_en.md

@@ -82,7 +82,7 @@ The following example takes a parsed annotation information to illustrate the co
 ```
 {
     'joints_vis': [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
-    'joints': [
+    'gt_joints': [
         [-1.0, -1.0],
         [-1.0, -1.0],
         [-1.0, -1.0],

ppdet/data/source/keypoint_coco.py

@@ -80,7 +80,8 @@ class KeypointBottomUpBaseDataset(DetDataset):
         records = copy.deepcopy(self._get_imganno(idx))
         records['image'] = cv2.imread(records['image_file'])
         records['image'] = cv2.cvtColor(records['image'], cv2.COLOR_BGR2RGB)
-        records['mask'] = (records['mask'] + 0).astype('uint8')
+        if 'mask' in records:
+            records['mask'] = (records['mask'] + 0).astype('uint8')
         records = self.transform(records)
         return records
 
@@ -135,24 +136,37 @@ class KeypointBottomUpCocoDataset(KeypointBottomUpBaseDataset):
                  num_joints,
                  transform=[],
                  shard=[0, 1],
-                 test_mode=False):
+                 test_mode=False,
+                 return_mask=True,
+                 return_bbox=True,
+                 return_area=True,
+                 return_class=True):
         super().__init__(dataset_dir, image_dir, anno_path, num_joints,
                          transform, shard, test_mode)
 
         self.ann_file = os.path.join(dataset_dir, anno_path)
         self.shard = shard
         self.test_mode = test_mode
+        self.return_mask = return_mask
+        self.return_bbox = return_bbox
+        self.return_area = return_area
+        self.return_class = return_class
 
     def parse_dataset(self):
         self.coco = COCO(self.ann_file)
 
         self.img_ids = self.coco.getImgIds()
         if not self.test_mode:
-            self.img_ids = [
-                img_id for img_id in self.img_ids
-                if len(self.coco.getAnnIds(
-                    imgIds=img_id, iscrowd=None)) > 0
-            ]
+            self.img_ids_tmp = []
+            for img_id in self.img_ids:
+                ann_ids = self.coco.getAnnIds(imgIds=img_id)
+                anno = self.coco.loadAnns(ann_ids)
+                anno = [obj for obj in anno if obj['iscrowd'] == 0]
+                if len(anno) == 0:
+                    continue
+                self.img_ids_tmp.append(img_id)
+            self.img_ids = self.img_ids_tmp
+
         blocknum = int(len(self.img_ids) / self.shard[1])
         self.img_ids = self.img_ids[(blocknum * self.shard[0]):(blocknum * (
             self.shard[0] + 1))]
@@ -199,21 +213,31 @@ class KeypointBottomUpCocoDataset(KeypointBottomUpBaseDataset):
         ann_ids = coco.getAnnIds(imgIds=img_id)
         anno = coco.loadAnns(ann_ids)
 
-        mask = self._get_mask(anno, idx)
         anno = [
             obj for obj in anno
-            if obj['iscrowd'] == 0 or obj['num_keypoints'] > 0
+            if obj['iscrowd'] == 0 and obj['num_keypoints'] > 0
         ]
 
+        db_rec = {}
         joints, orgsize = self._get_joints(anno, idx)
+        db_rec['gt_joints'] = joints
+        db_rec['im_shape'] = orgsize
+
+        if self.return_bbox:
+            db_rec['gt_bbox'] = self._get_bboxs(anno, idx)
+
+        if self.return_class:
+            db_rec['gt_class'] = self._get_labels(anno, idx)
+
+        if self.return_area:
+            db_rec['gt_areas'] = self._get_areas(anno, idx)
+
+        if self.return_mask:
+            db_rec['mask'] = self._get_mask(anno, idx)
 
-        db_rec = {}
         db_rec['im_id'] = img_id
         db_rec['image_file'] = os.path.join(self.img_prefix,
                                             self.id2name[img_id])
-        db_rec['mask'] = mask
-        db_rec['joints'] = joints
-        db_rec['im_shape'] = orgsize
 
         return db_rec
 
@@ -229,12 +253,41 @@ class KeypointBottomUpCocoDataset(KeypointBottomUpBaseDataset):
                 np.array(obj['keypoints']).reshape([-1, 3])
 
         img_info = self.coco.loadImgs(self.img_ids[idx])[0]
-        joints[..., 0] /= img_info['width']
-        joints[..., 1] /= img_info['height']
-        orgsize = np.array([img_info['height'], img_info['width']])
+        orgsize = np.array([img_info['height'], img_info['width'], 1])
 
         return joints, orgsize
 
+    def _get_bboxs(self, anno, idx):
+        num_people = len(anno)
+        gt_bboxes = np.zeros((num_people, 4), dtype=np.float32)
+
+        for idx, obj in enumerate(anno):
+            if 'bbox' in obj:
+                gt_bboxes[idx, :] = obj['bbox']
+
+        gt_bboxes[:, 2] += gt_bboxes[:, 0]
+        gt_bboxes[:, 3] += gt_bboxes[:, 1]
+        return gt_bboxes
+
+    def _get_labels(self, anno, idx):
+        num_people = len(anno)
+        gt_labels = np.zeros((num_people, 1), dtype=np.float32)
+
+        for idx, obj in enumerate(anno):
+            if 'category_id' in obj:
+                catid = obj['category_id']
+                gt_labels[idx, 0] = self.catid2clsid[catid]
+        return gt_labels
+
+    def _get_areas(self, anno, idx):
+        num_people = len(anno)
+        gt_areas = np.zeros((num_people, ), dtype=np.float32)
+
+        for idx, obj in enumerate(anno):
+            if 'area' in obj:
+                gt_areas[idx, ] = obj['area']
+        return gt_areas
+
     def _get_mask(self, anno, idx):
         """Get ignore masks to mask out losses."""
         coco = self.coco
@@ -506,7 +559,7 @@ class KeypointTopDownCocoDataset(KeypointTopDownBaseDataset):
                     'image_file': os.path.join(self.img_prefix, file_name),
                     'center': center,
                     'scale': scale,
-                    'joints': joints,
+                    'gt_joints': joints,
                     'joints_vis': joints_vis,
                     'im_id': im_id,
                 })
@@ -570,7 +623,7 @@ class KeypointTopDownCocoDataset(KeypointTopDownBaseDataset):
                 'center': center,
                 'scale': scale,
                 'score': score,
-                'joints': joints,
+                'gt_joints': joints,
                 'joints_vis': joints_vis,
             })
 
@@ -647,8 +700,8 @@ class KeypointTopDownMPIIDataset(KeypointTopDownBaseDataset):
                 (self.ann_info['num_joints'], 3), dtype=np.float32)
             joints_vis = np.zeros(
                 (self.ann_info['num_joints'], 3), dtype=np.float32)
-            if 'joints' in a:
-                joints_ = np.array(a['joints'])
+            if 'gt_joints' in a:
+                joints_ = np.array(a['gt_joints'])
                 joints_[:, 0:2] = joints_[:, 0:2] - 1
                 joints_vis_ = np.array(a['joints_vis'])
                 assert len(joints_) == self.ann_info[
@@ -664,7 +717,7 @@ class KeypointTopDownMPIIDataset(KeypointTopDownBaseDataset):
                 'im_id': im_id,
                 'center': c,
                 'scale': s,
-                'joints': joints,
+                'gt_joints': joints,
                 'joints_vis': joints_vis
             })
         print("number length: {}".format(len(gt_db)))

ppdet/data/transform/batch_operators.py

@@ -1102,13 +1102,115 @@ class PadGT(BaseOperator):
                                 1 means bbox, 0 means no bbox.
     """
 
-    def __init__(self, return_gt_mask=True):
+    def __init__(self, return_gt_mask=True, pad_img=False, minimum_gtnum=0):
         super(PadGT, self).__init__()
         self.return_gt_mask = return_gt_mask
+        self.pad_img = pad_img
+        self.minimum_gtnum = minimum_gtnum
+
+    def _impad(self, img: np.ndarray,
+            *,
+            shape = None,
+            padding = None,
+            pad_val = 0,
+            padding_mode = 'constant') -> np.ndarray:
+        """Pad the given image to a certain shape or pad on all sides with
+        specified padding mode and padding value.
+
+        Args:
+            img (ndarray): Image to be padded.
+            shape (tuple[int]): Expected padding shape (h, w). Default: None.
+            padding (int or tuple[int]): Padding on each border. If a single int is
+                provided this is used to pad all borders. If tuple of length 2 is
+                provided this is the padding on left/right and top/bottom
+                respectively. If a tuple of length 4 is provided this is the
+                padding for the left, top, right and bottom borders respectively.
+                Default: None. Note that `shape` and `padding` can not be both
+                set.
+            pad_val (Number | Sequence[Number]): Values to be filled in padding
+                areas when padding_mode is 'constant'. Default: 0.
+            padding_mode (str): Type of padding. Should be: constant, edge,
+                reflect or symmetric. Default: constant.
+                - constant: pads with a constant value, this value is specified
+                with pad_val.
+                - edge: pads with the last value at the edge of the image.
+                - reflect: pads with reflection of image without repeating the last
+                value on the edge. For example, padding [1, 2, 3, 4] with 2
+                elements on both sides in reflect mode will result in
+                [3, 2, 1, 2, 3, 4, 3, 2].
+                - symmetric: pads with reflection of image repeating the last value
+                on the edge. For example, padding [1, 2, 3, 4] with 2 elements on
+                both sides in symmetric mode will result in
+                [2, 1, 1, 2, 3, 4, 4, 3]
+
+        Returns:
+            ndarray: The padded image.
+        """
+
+        assert (shape is not None) ^ (padding is not None)
+        if shape is not None:
+            width = max(shape[1] - img.shape[1], 0)
+            height = max(shape[0] - img.shape[0], 0)
+            padding = (0, 0, int(width), int(height))
+
+        # check pad_val
+        import numbers
+        if isinstance(pad_val, tuple):
+            assert len(pad_val) == img.shape[-1]
+        elif not isinstance(pad_val, numbers.Number):
+            raise TypeError('pad_val must be a int or a tuple. '
+                            f'But received {type(pad_val)}')
+
+        # check padding
+        if isinstance(padding, tuple) and len(padding) in [2, 4]:
+            if len(padding) == 2:
+                padding = (padding[0], padding[1], padding[0], padding[1])
+        elif isinstance(padding, numbers.Number):
+            padding = (padding, padding, padding, padding)
+        else:
+            raise ValueError('Padding must be a int or a 2, or 4 element tuple.'
+                            f'But received {padding}')
+
+        # check padding mode
+        assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric']
+
+        border_type = {
+            'constant': cv2.BORDER_CONSTANT,
+            'edge': cv2.BORDER_REPLICATE,
+            'reflect': cv2.BORDER_REFLECT_101,
+            'symmetric': cv2.BORDER_REFLECT
+        }
+        img = cv2.copyMakeBorder(
+            img,
+            padding[1],
+            padding[3],
+            padding[0],
+            padding[2],
+            border_type[padding_mode],
+            value=pad_val)
+
+        return img
+
+    def checkmaxshape(self, samples):
+        maxh, maxw = 0, 0
+        for sample in samples:
+            h,w = sample['im_shape']
+            if h>maxh:
+                maxh = h
+            if w>maxw:
+                maxw = w
+        return (maxh, maxw)
 
     def __call__(self, samples, context=None):
         num_max_boxes = max([len(s['gt_bbox']) for s in samples])
+        num_max_boxes = max(self.minimum_gtnum, num_max_boxes)
+        if self.pad_img:
+            maxshape = self.checkmaxshape(samples)
         for sample in samples:
+            if self.pad_img:
+                img = sample['image']
+                padimg = self._impad(img, shape=maxshape)
+                sample['image'] = padimg
             if self.return_gt_mask:
                 sample['pad_gt_mask'] = np.zeros(
                     (num_max_boxes, 1), dtype=np.float32)
@@ -1142,6 +1244,17 @@ class PadGT(BaseOperator):
                 if num_gt > 0:
                     pad_diff[:num_gt] = sample['difficult']
                 sample['difficult'] = pad_diff
+            if 'gt_joints' in sample:
+                num_joints = sample['gt_joints'].shape[1]
+                pad_gt_joints = np.zeros((num_max_boxes, num_joints, 3), dtype=np.float32)
+                if num_gt > 0:
+                    pad_gt_joints[:num_gt] = sample['gt_joints']
+                sample['gt_joints'] = pad_gt_joints
+            if 'gt_areas' in sample:
+                pad_gt_areas = np.zeros((num_max_boxes, 1), dtype=np.float32)
+                if num_gt > 0:
+                    pad_gt_areas[:num_gt, 0] = sample['gt_areas']
+                sample['gt_areas'] = pad_gt_areas
         return samples
 
 

ppdet/data/transform/operators.py

@@ -594,6 +594,108 @@ class RandomDistort(BaseOperator):
         return sample
 
 
+@register_op
+class PhotoMetricDistortion(BaseOperator):
+    """Apply photometric distortion to image sequentially, every transformation
+    is applied with a probability of 0.5. The position of random contrast is in
+    second or second to last.
+
+    1. random brightness
+    2. random contrast (mode 0)
+    3. convert color from BGR to HSV
+    4. random saturation
+    5. random hue
+    6. convert color from HSV to BGR
+    7. random contrast (mode 1)
+    8. randomly swap channels
+
+    Args:
+        brightness_delta (int): delta of brightness.
+        contrast_range (tuple): range of contrast.
+        saturation_range (tuple): range of saturation.
+        hue_delta (int): delta of hue.
+    """
+
+    def __init__(self,
+                 brightness_delta=32,
+                 contrast_range=(0.5, 1.5),
+                 saturation_range=(0.5, 1.5),
+                 hue_delta=18):
+        super(PhotoMetricDistortion, self).__init__()
+        self.brightness_delta = brightness_delta
+        self.contrast_lower, self.contrast_upper = contrast_range
+        self.saturation_lower, self.saturation_upper = saturation_range
+        self.hue_delta = hue_delta
+
+    def apply(self, results, context=None):
+        """Call function to perform photometric distortion on images.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Result dict with images distorted.
+        """
+
+        img = results['image']
+        img = img.astype(np.float32)
+        # random brightness
+        if np.random.randint(2):
+            delta = np.random.uniform(-self.brightness_delta,
+                                      self.brightness_delta)
+            img += delta
+
+        # mode == 0 --> do random contrast first
+        # mode == 1 --> do random contrast last
+        mode = np.random.randint(2)
+        if mode == 1:
+            if np.random.randint(2):
+                alpha = np.random.uniform(self.contrast_lower,
+                                          self.contrast_upper)
+                img *= alpha
+
+        # convert color from BGR to HSV
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+
+        # random saturation
+        if np.random.randint(2):
+            img[..., 1] *= np.random.uniform(self.saturation_lower,
+                                             self.saturation_upper)
+
+        # random hue
+        if np.random.randint(2):
+            img[..., 0] += np.random.uniform(-self.hue_delta, self.hue_delta)
+            img[..., 0][img[..., 0] > 360] -= 360
+            img[..., 0][img[..., 0] < 0] += 360
+
+        # convert color from HSV to BGR
+        img = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)
+
+        # random contrast
+        if mode == 0:
+            if np.random.randint(2):
+                alpha = np.random.uniform(self.contrast_lower,
+                                          self.contrast_upper)
+                img *= alpha
+
+        # randomly swap channels
+        if np.random.randint(2):
+            img = img[..., np.random.permutation(3)]
+
+        results['image'] = img
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(\nbrightness_delta={self.brightness_delta},\n'
+        repr_str += 'contrast_range='
+        repr_str += f'{(self.contrast_lower, self.contrast_upper)},\n'
+        repr_str += 'saturation_range='
+        repr_str += f'{(self.saturation_lower, self.saturation_upper)},\n'
+        repr_str += f'hue_delta={self.hue_delta})'
+        return repr_str
+
+
 @register_op
 class AutoAugment(BaseOperator):
     def __init__(self, autoaug_type="v1"):
@@ -771,6 +873,19 @@ class Resize(BaseOperator):
         bbox[:, 1::2] = np.clip(bbox[:, 1::2], 0, resize_h)
         return bbox
 
+    def apply_area(self, area, scale):
+        im_scale_x, im_scale_y = scale
+        return area * im_scale_x * im_scale_y
+
+    def apply_joints(self, joints, scale, size):
+        im_scale_x, im_scale_y = scale
+        resize_w, resize_h = size
+        joints[..., 0] *= im_scale_x
+        joints[..., 1] *= im_scale_y
+        joints[..., 0] = np.clip(joints[..., 0], 0, resize_w)
+        joints[..., 1] = np.clip(joints[..., 1], 0, resize_h)
+        return joints
+
     def apply_segm(self, segms, im_size, scale):
         def _resize_poly(poly, im_scale_x, im_scale_y):
             resized_poly = np.array(poly).astype('float32')
@@ -833,8 +948,8 @@ class Resize(BaseOperator):
             im_scale = min(target_size_min / im_size_min,
                            target_size_max / im_size_max)
 
-            resize_h = im_scale * float(im_shape[0])
-            resize_w = im_scale * float(im_shape[1])
+            resize_h = int(im_scale * float(im_shape[0]) + 0.5)
+            resize_w = int(im_scale * float(im_shape[1]) + 0.5)
 
             im_scale_x = im_scale
             im_scale_y = im_scale
@@ -878,6 +993,11 @@ class Resize(BaseOperator):
                                                 [im_scale_x, im_scale_y],
                                                 [resize_w, resize_h])
 
+        # apply areas
+        if 'gt_areas' in sample:
+            sample['gt_areas'] = self.apply_area(sample['gt_areas'],
+                                                 [im_scale_x, im_scale_y])
+
         # apply polygon
         if 'gt_poly' in sample and len(sample['gt_poly']) > 0:
             sample['gt_poly'] = self.apply_segm(sample['gt_poly'], im_shape[:2],
@@ -911,6 +1031,11 @@ class Resize(BaseOperator):
             ]
             sample['gt_segm'] = np.asarray(masks).astype(np.uint8)
 
+        if 'gt_joints' in sample:
+            sample['gt_joints'] = self.apply_joints(sample['gt_joints'],
+                                                    [im_scale_x, im_scale_y],
+                                                    [resize_w, resize_h])
+
         return sample
 
 
@@ -1362,7 +1487,8 @@ class RandomCrop(BaseOperator):
                  num_attempts=50,
                  allow_no_crop=True,
                  cover_all_box=False,
-                 is_mask_crop=False):
+                 is_mask_crop=False,
+                 ioumode="iou"):
         super(RandomCrop, self).__init__()
         self.aspect_ratio = aspect_ratio
         self.thresholds = thresholds
@@ -1371,6 +1497,7 @@ class RandomCrop(BaseOperator):
         self.allow_no_crop = allow_no_crop
         self.cover_all_box = cover_all_box
         self.is_mask_crop = is_mask_crop
+        self.ioumode = ioumode
 
     def crop_segms(self, segms, valid_ids, crop, height, width):
         def _crop_poly(segm, crop):
@@ -1516,9 +1643,14 @@ class RandomCrop(BaseOperator):
                 crop_y = np.random.randint(0, h - crop_h)
                 crop_x = np.random.randint(0, w - crop_w)
                 crop_box = [crop_x, crop_y, crop_x + crop_w, crop_y + crop_h]
-                iou = self._iou_matrix(
-                    gt_bbox, np.array(
-                        [crop_box], dtype=np.float32))
+                if self.ioumode == "iof":
+                    iou = self._gtcropiou_matrix(
+                        gt_bbox, np.array(
+                            [crop_box], dtype=np.float32))
+                elif self.ioumode == "iou":
+                    iou = self._iou_matrix(
+                        gt_bbox, np.array(
+                            [crop_box], dtype=np.float32))
                 if iou.max() < thresh:
                     continue
 
@@ -1582,6 +1714,10 @@ class RandomCrop(BaseOperator):
                     sample['difficult'] = np.take(
                         sample['difficult'], valid_ids, axis=0)
 
+                if 'gt_joints' in sample:
+                    sample['gt_joints'] = self._crop_joints(sample['gt_joints'],
+                                                            crop_box)
+
                 return sample
 
         return sample
@@ -1596,6 +1732,16 @@ class RandomCrop(BaseOperator):
         area_o = (area_a[:, np.newaxis] + area_b - area_i)
         return area_i / (area_o + 1e-10)
 
+    def _gtcropiou_matrix(self, a, b):
+        tl_i = np.maximum(a[:, np.newaxis, :2], b[:, :2])
+        br_i = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
+
+        area_i = np.prod(br_i - tl_i, axis=2) * (tl_i < br_i).all(axis=2)
+        area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
+        area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)
+        area_o = (area_a[:, np.newaxis] + area_b - area_i)
+        return area_i / (area_a + 1e-10)
+
     def _crop_box_with_center_constraint(self, box, crop):
         cropped_box = box.copy()
 
@@ -1620,6 +1766,16 @@ class RandomCrop(BaseOperator):
         x1, y1, x2, y2 = crop
         return segm[:, y1:y2, x1:x2]
 
+    def _crop_joints(self, joints, crop):
+        x1, y1, x2, y2 = crop
+        joints[joints[..., 0] > x2, :] = 0
+        joints[joints[..., 1] > y2, :] = 0
+        joints[joints[..., 0] < x1, :] = 0
+        joints[joints[..., 1] < y1, :] = 0
+        joints[..., 0] -= x1
+        joints[..., 1] -= y1
+        return joints
+
 
 @register_op
 class RandomScaledCrop(BaseOperator):
@@ -1648,8 +1804,8 @@ class RandomScaledCrop(BaseOperator):
         random_dim = int(dim * random_scale)
         dim_max = max(h, w)
         scale = random_dim / dim_max
-        resize_w = w * scale
-        resize_h = h * scale
+        resize_w = int(w * scale + 0.5)
+        resize_h = int(h * scale + 0.5)
         offset_x = int(max(0, np.random.uniform(0., resize_w - dim)))
         offset_y = int(max(0, np.random.uniform(0., resize_h - dim)))
 
@@ -2316,25 +2472,26 @@ class RandomResizeCrop(BaseOperator):
         is_mask_crop(bool): whether crop the segmentation.
     """
 
-    def __init__(
-            self,
-            resizes,
-            cropsizes,
-            prob=0.5,
-            mode='short',
-            keep_ratio=True,
-            interp=cv2.INTER_LINEAR,
-            num_attempts=3,
-            cover_all_box=False,
-            allow_no_crop=False,
-            thresholds=[0.3, 0.5, 0.7],
-            is_mask_crop=False, ):
+    def __init__(self,
+                 resizes,
+                 cropsizes,
+                 prob=0.5,
+                 mode='short',
+                 keep_ratio=True,
+                 interp=cv2.INTER_LINEAR,
+                 num_attempts=3,
+                 cover_all_box=False,
+                 allow_no_crop=False,
+                 thresholds=[0.3, 0.5, 0.7],
+                 is_mask_crop=False,
+                 ioumode="iou"):
         super(RandomResizeCrop, self).__init__()
 
         self.resizes = resizes
         self.cropsizes = cropsizes
         self.prob = prob
         self.mode = mode
+        self.ioumode = ioumode
 
         self.resizer = Resize(0, keep_ratio=keep_ratio, interp=interp)
         self.croper = RandomCrop(
@@ -2389,9 +2546,14 @@ class RandomResizeCrop(BaseOperator):
                 crop_x = random.randint(0, w - crop_w)
 
                 crop_box = [crop_x, crop_y, crop_x + crop_w, crop_y + crop_h]
-                iou = self._iou_matrix(
-                    gt_bbox, np.array(
-                        [crop_box], dtype=np.float32))
+                if self.ioumode == "iof":
+                    iou = self._gtcropiou_matrix(
+                        gt_bbox, np.array(
+                            [crop_box], dtype=np.float32))
+                elif self.ioumode == "iou":
+                    iou = self._iou_matrix(
+                        gt_bbox, np.array(
+                            [crop_box], dtype=np.float32))
                 if iou.max() < thresh:
                     continue
 
@@ -2447,6 +2609,14 @@ class RandomResizeCrop(BaseOperator):
                 if 'is_crowd' in sample:
                     sample['is_crowd'] = np.take(
                         sample['is_crowd'], valid_ids, axis=0)
+
+                if 'gt_areas' in sample:
+                    sample['gt_areas'] = np.take(
+                        sample['gt_areas'], valid_ids, axis=0)
+
+                if 'gt_joints' in sample:
+                    gt_joints = self._crop_joints(sample['gt_joints'], crop_box)
+                    sample['gt_joints'] = gt_joints[valid_ids]
                 return sample
 
         return sample
@@ -2479,8 +2649,8 @@ class RandomResizeCrop(BaseOperator):
                 im_scale = max(target_size_min / im_size_min,
                                target_size_max / im_size_max)
 
-            resize_h = im_scale * float(im_shape[0])
-            resize_w = im_scale * float(im_shape[1])
+            resize_h = int(im_scale * float(im_shape[0]) + 0.5)
+            resize_w = int(im_scale * float(im_shape[1]) + 0.5)
 
             im_scale_x = im_scale
             im_scale_y = im_scale
@@ -2540,6 +2710,11 @@ class RandomResizeCrop(BaseOperator):
             ]
             sample['gt_segm'] = np.asarray(masks).astype(np.uint8)
 
+        if 'gt_joints' in sample:
+            sample['gt_joints'] = self.apply_joints(sample['gt_joints'],
+                                                    [im_scale_x, im_scale_y],
+                                                    [resize_w, resize_h])
+
         return sample
 
 
@@ -2612,10 +2787,10 @@ class RandomShortSideResize(BaseOperator):
 
         if w < h:
             ow = size
-            oh = int(size * h / w)
+            oh = int(round(size * h / w))
         else:
             oh = size
-            ow = int(size * w / h)
+            ow = int(round(size * w / h))
 
         return (ow, oh)
 
@@ -2672,6 +2847,16 @@ class RandomShortSideResize(BaseOperator):
                 for gt_segm in sample['gt_segm']
             ]
             sample['gt_segm'] = np.asarray(masks).astype(np.uint8)
+
+        if 'gt_joints' in sample:
+            sample['gt_joints'] = self.apply_joints(
+                sample['gt_joints'], [im_scale_x, im_scale_y], target_size)
+
+        # apply areas
+        if 'gt_areas' in sample:
+            sample['gt_areas'] = self.apply_area(sample['gt_areas'],
+                                                 [im_scale_x, im_scale_y])
+
         return sample
 
     def apply_bbox(self, bbox, scale, size):
@@ -2683,6 +2868,23 @@ class RandomShortSideResize(BaseOperator):
         bbox[:, 1::2] = np.clip(bbox[:, 1::2], 0, resize_h)
         return bbox.astype('float32')
 
+    def apply_joints(self, joints, scale, size):
+        im_scale_x, im_scale_y = scale
+        resize_w, resize_h = size
+        joints[..., 0] *= im_scale_x
+        joints[..., 1] *= im_scale_y
+        # joints[joints[..., 0] >= resize_w, :] = 0
+        # joints[joints[..., 1] >= resize_h, :] = 0
+        # joints[joints[..., 0] < 0, :] = 0
+        # joints[joints[..., 1] < 0, :] = 0
+        joints[..., 0] = np.clip(joints[..., 0], 0, resize_w)
+        joints[..., 1] = np.clip(joints[..., 1], 0, resize_h)
+        return joints
+
+    def apply_area(self, area, scale):
+        im_scale_x, im_scale_y = scale
+        return area * im_scale_x * im_scale_y
+
     def apply_segm(self, segms, im_size, scale):
         def _resize_poly(poly, im_scale_x, im_scale_y):
             resized_poly = np.array(poly).astype('float32')
@@ -2730,6 +2932,44 @@ class RandomShortSideResize(BaseOperator):
         return self.resize(sample, target_size, self.max_size, interp)
 
 
+@register_op
+class RandomShortSideRangeResize(RandomShortSideResize):
+    def __init__(self, scales, interp=cv2.INTER_LINEAR, random_interp=False):
+        """
+        Resize the image randomly according to the short side. If max_size is not None,
+        the long side is scaled according to max_size. The whole process will be keep ratio.
+        Args:
+            short_side_sizes (list|tuple): Image target short side size.
+            interp (int): The interpolation method.
+            random_interp (bool): Whether random select interpolation method.
+        """
+        super(RandomShortSideRangeResize, self).__init__(scales, None, interp,
+                                                         random_interp)
+
+        assert isinstance(scales,
+                          Sequence), "short_side_sizes must be List or Tuple"
+
+        self.scales = scales
+
+    def random_sample(self, img_scales):
+        img_scale_long = [max(s) for s in img_scales]
+        img_scale_short = [min(s) for s in img_scales]
+        long_edge = np.random.randint(
+            min(img_scale_long), max(img_scale_long) + 1)
+        short_edge = np.random.randint(
+            min(img_scale_short), max(img_scale_short) + 1)
+        img_scale = (long_edge, short_edge)
+        return img_scale
+
+    def apply(self, sample, context=None):
+        long_edge, short_edge = self.random_sample(self.short_side_sizes)
+        # print("target size:{}".format((long_edge, short_edge)))
+        interp = random.choice(
+            self.interps) if self.random_interp else self.interp
+
+        return self.resize(sample, short_edge, long_edge, interp)
+
+
 @register_op
 class RandomSizeCrop(BaseOperator):
     """
@@ -2805,6 +3045,9 @@ class RandomSizeCrop(BaseOperator):
                 sample['is_crowd'] = sample['is_crowd'][keep_index] if len(
                     keep_index) > 0 else np.zeros(
                         [0, 1], dtype=np.float32)
+            if 'gt_areas' in sample:
+                sample['gt_areas'] = np.take(
+                    sample['gt_areas'], keep_index, axis=0)
 
         image_shape = sample['image'].shape[:2]
         sample['image'] = self.paddle_crop(sample['image'], *region)
@@ -2826,6 +3069,12 @@ class RandomSizeCrop(BaseOperator):
             if keep_index is not None and len(keep_index) > 0:
                 sample['gt_segm'] = sample['gt_segm'][keep_index]
 
+        if 'gt_joints' in sample:
+            gt_joints = self._crop_joints(sample['gt_joints'], region)
+            sample['gt_joints'] = gt_joints
+            if keep_index is not None:
+                sample['gt_joints'] = sample['gt_joints'][keep_index]
+
         return sample
 
     def apply_bbox(self, bbox, region):
@@ -2836,6 +3085,19 @@ class RandomSizeCrop(BaseOperator):
         crop_bbox = crop_bbox.clip(min=0)
         return crop_bbox.reshape([-1, 4]).astype('float32')
 
+    def _crop_joints(self, joints, region):
+        y1, x1, h, w = region
+        x2 = x1 + w
+        y2 = y1 + h
+        # x1, y1, x2, y2 = crop
+        joints[..., 0] -= x1
+        joints[..., 1] -= y1
+        joints[joints[..., 0] > w, :] = 0
+        joints[joints[..., 1] > h, :] = 0
+        joints[joints[..., 0] < 0, :] = 0
+        joints[joints[..., 1] < 0, :] = 0
+        return joints
+
     def apply_segm(self, segms, region, image_shape):
         def _crop_poly(segm, crop):
             xmin, ymin, xmax, ymax = crop

ppdet/modeling/architectures/__init__.py

@@ -72,3 +72,4 @@ from .yolof import *
 from .pose3d_metro import *
 from .centertrack import *
 from .queryinst import *
+from .keypoint_petr import *

ppdet/modeling/architectures/keypoint_hrnet.py

@@ -394,6 +394,7 @@ class TinyPose3DHRNet(BaseArch):
     def __init__(self,
                  width,
                  num_joints,
+                 fc_channel=768,
                  backbone='HRNet',
                  loss='KeyPointRegressionMSELoss',
                  post_process=TinyPose3DPostProcess):
@@ -411,21 +412,13 @@ class TinyPose3DHRNet(BaseArch):
 
         self.final_conv = L.Conv2d(width, num_joints, 1, 1, 0, bias=True)
 
-        self.final_conv_new = L.Conv2d(
-            width, num_joints * 32, 1, 1, 0, bias=True)
-
         self.flatten = paddle.nn.Flatten(start_axis=2, stop_axis=3)
-        self.fc1 = paddle.nn.Linear(768, 256)
+        self.fc1 = paddle.nn.Linear(fc_channel, 256)
         self.act1 = paddle.nn.ReLU()
         self.fc2 = paddle.nn.Linear(256, 64)
         self.act2 = paddle.nn.ReLU()
         self.fc3 = paddle.nn.Linear(64, 3)
 
-        # for human3.6M
-        self.fc1_1 = paddle.nn.Linear(3136, 1024)
-        self.fc2_1 = paddle.nn.Linear(1024, 256)
-        self.fc3_1 = paddle.nn.Linear(256, 3)
-
     @classmethod
     def from_config(cls, cfg, *args, **kwargs):
         # backbone

ppdet/modeling/architectures/keypoint_petr.py

+# Copyright (c) 2023 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.
+"""
+this code is base on https://github.com/hikvision-research/opera/blob/main/opera/models/detectors/petr.py
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from ppdet.core.workspace import register
+from .meta_arch import BaseArch
+from .. import layers as L
+
+__all__ = ['PETR']
+
+
+@register
+class PETR(BaseArch):
+    __category__ = 'architecture'
+    __inject__ = ['backbone', 'neck', 'bbox_head']
+
+    def __init__(self,
+                 backbone='ResNet',
+                 neck='ChannelMapper',
+                 bbox_head='PETRHead'):
+        """
+        PETR, see https://openaccess.thecvf.com/content/CVPR2022/papers/Shi_End-to-End_Multi-Person_Pose_Estimation_With_Transformers_CVPR_2022_paper.pdf
+
+        Args:
+            backbone (nn.Layer): backbone instance
+            neck (nn.Layer): neck between backbone and head
+            bbox_head (nn.Layer): model output and loss
+        """
+        super(PETR, self).__init__()
+        self.backbone = backbone
+        if neck is not None:
+            self.with_neck = True
+        self.neck = neck
+        self.bbox_head = bbox_head
+        self.deploy = False
+
+    def extract_feat(self, img):
+        """Directly extract features from the backbone+neck."""
+        x = self.backbone(img)
+        if self.with_neck:
+            x = self.neck(x)
+        return x
+
+    def get_inputs(self):
+        img_metas = []
+        gt_bboxes = []
+        gt_labels = []
+        gt_keypoints = []
+        gt_areas = []
+        pad_gt_mask = self.inputs['pad_gt_mask'].astype("bool").squeeze(-1)
+        for idx, im_shape in enumerate(self.inputs['im_shape']):
+            img_meta = {
+                'img_shape': im_shape.astype("int32").tolist() + [1, ],
+                'batch_input_shape': self.inputs['image'].shape[-2:],
+                'image_name': self.inputs['image_file'][idx]
+            }
+            img_metas.append(img_meta)
+            if (not pad_gt_mask[idx].any()):
+                gt_keypoints.append(self.inputs['gt_joints'][idx][:1])
+                gt_labels.append(self.inputs['gt_class'][idx][:1])
+                gt_bboxes.append(self.inputs['gt_bbox'][idx][:1])
+                gt_areas.append(self.inputs['gt_areas'][idx][:1])
+                continue
+
+            gt_keypoints.append(self.inputs['gt_joints'][idx][pad_gt_mask[idx]])
+            gt_labels.append(self.inputs['gt_class'][idx][pad_gt_mask[idx]])
+            gt_bboxes.append(self.inputs['gt_bbox'][idx][pad_gt_mask[idx]])
+            gt_areas.append(self.inputs['gt_areas'][idx][pad_gt_mask[idx]])
+
+        return img_metas, gt_bboxes, gt_labels, gt_keypoints, gt_areas
+
+    def get_loss(self):
+        """
+        Args:
+            img (Tensor): Input images of shape (N, C, H, W).
+                Typically these should be mean centered and std scaled.
+            img_metas (list[dict]): A List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                :class:`mmdet.datasets.pipelines.Collect`.
+            gt_bboxes (list[Tensor]): Each item are the truth boxes for each
+                image in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): Class indices corresponding to each box.
+            gt_keypoints (list[Tensor]): Each item are the truth keypoints for
+                each image in [p^{1}_x, p^{1}_y, p^{1}_v, ..., p^{K}_x,
+                p^{K}_y, p^{K}_v] format.
+            gt_areas (list[Tensor]): mask areas corresponding to each box.
+            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+
+        img_metas, gt_bboxes, gt_labels, gt_keypoints, gt_areas = self.get_inputs(
+        )
+        gt_bboxes_ignore = getattr(self.inputs, 'gt_bboxes_ignore', None)
+
+        x = self.extract_feat(self.inputs)
+        losses = self.bbox_head.forward_train(x, img_metas, gt_bboxes,
+                                              gt_labels, gt_keypoints, gt_areas,
+                                              gt_bboxes_ignore)
+        loss = 0
+        for k, v in losses.items():
+            loss += v
+        losses['loss'] = loss
+
+        return losses
+
+    def get_pred_numpy(self):
+        """Used for computing network flops.
+        """
+
+        img = self.inputs['image']
+        batch_size, _, height, width = img.shape
+        dummy_img_metas = [
+            dict(
+                batch_input_shape=(height, width),
+                img_shape=(height, width, 3),
+                scale_factor=(1., 1., 1., 1.)) for _ in range(batch_size)
+        ]
+        x = self.extract_feat(img)
+        outs = self.bbox_head(x, img_metas=dummy_img_metas)
+        bbox_list = self.bbox_head.get_bboxes(
+            *outs, dummy_img_metas, rescale=True)
+        return bbox_list
+
+    def get_pred(self):
+        """
+        """
+        img = self.inputs['image']
+        batch_size, _, height, width = img.shape
+        img_metas = [
+            dict(
+                batch_input_shape=(height, width),
+                img_shape=(height, width, 3),
+                scale_factor=self.inputs['scale_factor'][i])
+            for i in range(batch_size)
+        ]
+        kptpred = self.simple_test(
+            self.inputs, img_metas=img_metas, rescale=True)
+        keypoints = kptpred[0][1][0]
+        bboxs = kptpred[0][0][0]
+        keypoints[..., 2] = bboxs[:, None, 4]
+        res_lst = [[keypoints, bboxs[:, 4]]]
+        outputs = {'keypoint': res_lst}
+        return outputs
+
+    def simple_test(self, inputs, img_metas, rescale=False):
+        """Test function without test time augmentation.
+
+        Args:
+            inputs (list[paddle.Tensor]): List of multiple images.
+            img_metas (list[dict]): List of image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[list[np.ndarray]]: BBox and keypoint results of each image
+                and classes. The outer list corresponds to each image.
+                The inner list corresponds to each class.
+        """
+        batch_size = len(img_metas)
+        assert batch_size == 1, 'Currently only batch_size 1 for inference ' \
+            f'mode is supported. Found batch_size {batch_size}.'
+        feat = self.extract_feat(inputs)
+        results_list = self.bbox_head.simple_test(
+            feat, img_metas, rescale=rescale)
+
+        bbox_kpt_results = [
+            self.bbox_kpt2result(det_bboxes, det_labels, det_kpts,
+                                 self.bbox_head.num_classes)
+            for det_bboxes, det_labels, det_kpts in results_list
+        ]
+        return bbox_kpt_results
+
+    def bbox_kpt2result(self, bboxes, labels, kpts, num_classes):
+        """Convert detection results to a list of numpy arrays.
+
+        Args:
+            bboxes (paddle.Tensor | np.ndarray): shape (n, 5).
+            labels (paddle.Tensor | np.ndarray): shape (n, ).
+            kpts (paddle.Tensor | np.ndarray): shape (n, K, 3).
+            num_classes (int): class number, including background class.
+
+        Returns:
+            list(ndarray): bbox and keypoint results of each class.
+        """
+        if bboxes.shape[0] == 0:
+            return [np.zeros((0, 5), dtype=np.float32) for i in range(num_classes)], \
+                [np.zeros((0, kpts.size(1), 3), dtype=np.float32)
+                    for i in range(num_classes)]
+        else:
+            if isinstance(bboxes, paddle.Tensor):
+                bboxes = bboxes.numpy()
+                labels = labels.numpy()
+                kpts = kpts.numpy()
+            return [bboxes[labels == i, :] for i in range(num_classes)], \
+                [kpts[labels == i, :, :] for i in range(num_classes)]

ppdet/modeling/assigners/__init__.py

@@ -31,3 +31,5 @@ from .fcosr_assigner import *
 from .rotated_task_aligned_assigner import *
 from .task_aligned_assigner_cr import *
 from .uniform_assigner import *
+from .hungarian_assigner import *
+from .pose_utils import *

ppdet/modeling/assigners/hungarian_assigner.py

+# Copyright (c) 2023 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
+
+try:
+    from scipy.optimize import linear_sum_assignment
+except ImportError:
+    linear_sum_assignment = None
+
+import paddle
+
+from ppdet.core.workspace import register
+
+__all__ = ['PoseHungarianAssigner', 'PseudoSampler']
+
+
+class AssignResult:
+    """Stores assignments between predicted and truth boxes.
+
+    Attributes:
+        num_gts (int): the number of truth boxes considered when computing this
+            assignment
+
+        gt_inds (LongTensor): for each predicted box indicates the 1-based
+            index of the assigned truth box. 0 means unassigned and -1 means
+            ignore.
+
+        max_overlaps (FloatTensor): the iou between the predicted box and its
+            assigned truth box.
+
+        labels (None | LongTensor): If specified, for each predicted box
+            indicates the category label of the assigned truth box.
+    """
+
+    def __init__(self, num_gts, gt_inds, max_overlaps, labels=None):
+        self.num_gts = num_gts
+        self.gt_inds = gt_inds
+        self.max_overlaps = max_overlaps
+        self.labels = labels
+        # Interface for possible user-defined properties
+        self._extra_properties = {}
+
+    @property
+    def num_preds(self):
+        """int: the number of predictions in this assignment"""
+        return len(self.gt_inds)
+
+    def set_extra_property(self, key, value):
+        """Set user-defined new property."""
+        assert key not in self.info
+        self._extra_properties[key] = value
+
+    def get_extra_property(self, key):
+        """Get user-defined property."""
+        return self._extra_properties.get(key, None)
+
+    @property
+    def info(self):
+        """dict: a dictionary of info about the object"""
+        basic_info = {
+            'num_gts': self.num_gts,
+            'num_preds': self.num_preds,
+            'gt_inds': self.gt_inds,
+            'max_overlaps': self.max_overlaps,
+            'labels': self.labels,
+        }
+        basic_info.update(self._extra_properties)
+        return basic_info
+
+
+@register
+class PoseHungarianAssigner:
+    """Computes one-to-one matching between predictions and ground truth.
+
+    This class computes an assignment between the targets and the predictions
+    based on the costs. The costs are weighted sum of three components:
+    classification cost, regression L1 cost and regression oks cost. The
+    targets don't include the no_object, so generally there are more
+    predictions than targets. After the one-to-one matching, the un-matched
+    are treated as backgrounds. Thus each query prediction will be assigned
+    with `0` or a positive integer indicating the ground truth index:
+
+    - 0: negative sample, no assigned gt.
+    - positive integer: positive sample, index (1-based) of assigned gt.
+
+    Args:
+        cls_weight (int | float, optional): The scale factor for classification
+            cost. Default 1.0.
+        kpt_weight (int | float, optional): The scale factor for regression
+            L1 cost. Default 1.0.
+        oks_weight (int | float, optional): The scale factor for regression
+            oks cost. Default 1.0.
+    """
+    __inject__ = ['cls_cost', 'kpt_cost', 'oks_cost']
+
+    def __init__(self,
+                 cls_cost='ClassificationCost',
+                 kpt_cost='KptL1Cost',
+                 oks_cost='OksCost'):
+        self.cls_cost = cls_cost
+        self.kpt_cost = kpt_cost
+        self.oks_cost = oks_cost
+
+    def assign(self,
+               cls_pred,
+               kpt_pred,
+               gt_labels,
+               gt_keypoints,
+               gt_areas,
+               img_meta,
+               eps=1e-7):
+        """Computes one-to-one matching based on the weighted costs.
+
+        This method assign each query prediction to a ground truth or
+        background. The `assigned_gt_inds` with -1 means don't care,
+        0 means negative sample, and positive number is the index (1-based)
+        of assigned gt.
+        The assignment is done in the following steps, the order matters.
+
+        1. assign every prediction to -1
+        2. compute the weighted costs
+        3. do Hungarian matching on CPU based on the costs
+        4. assign all to 0 (background) first, then for each matched pair
+           between predictions and gts, treat this prediction as foreground
+           and assign the corresponding gt index (plus 1) to it.
+
+        Args:
+            cls_pred (Tensor): Predicted classification logits, shape
+                [num_query, num_class].
+            kpt_pred (Tensor): Predicted keypoints with normalized coordinates
+                (x_{i}, y_{i}), which are all in range [0, 1]. Shape
+                [num_query, K*2].
+            gt_labels (Tensor): Label of `gt_keypoints`, shape (num_gt,).
+            gt_keypoints (Tensor): Ground truth keypoints with unnormalized
+                coordinates [p^{1}_x, p^{1}_y, p^{1}_v, ..., \
+                    p^{K}_x, p^{K}_y, p^{K}_v]. Shape [num_gt, K*3].
+            gt_areas (Tensor): Ground truth mask areas, shape (num_gt,).
+            img_meta (dict): Meta information for current image.
+            eps (int | float, optional): A value added to the denominator for
+                numerical stability. Default 1e-7.
+
+        Returns:
+            :obj:`AssignResult`: The assigned result.
+        """
+        num_gts, num_kpts = gt_keypoints.shape[0], kpt_pred.shape[0]
+        if not gt_keypoints.astype('bool').any():
+            num_gts = 0
+
+        # 1. assign -1 by default
+        assigned_gt_inds = paddle.full((num_kpts, ), -1, dtype="int64")
+        assigned_labels = paddle.full((num_kpts, ), -1, dtype="int64")
+        if num_gts == 0 or num_kpts == 0:
+            # No ground truth or keypoints, return empty assignment
+            if num_gts == 0:
+                # No ground truth, assign all to background
+                assigned_gt_inds[:] = 0
+            return AssignResult(
+                num_gts, assigned_gt_inds, None, labels=assigned_labels)
+        img_h, img_w, _ = img_meta['img_shape']
+        factor = paddle.to_tensor(
+            [img_w, img_h, img_w, img_h], dtype=gt_keypoints.dtype).reshape(
+                (1, -1))
+
+        # 2. compute the weighted costs
+        # classification cost
+        cls_cost = self.cls_cost(cls_pred, gt_labels)
+
+        # keypoint regression L1 cost
+        gt_keypoints_reshape = gt_keypoints.reshape((gt_keypoints.shape[0], -1,
+                                                     3))
+        valid_kpt_flag = gt_keypoints_reshape[..., -1]
+        kpt_pred_tmp = kpt_pred.clone().detach().reshape((kpt_pred.shape[0], -1,
+                                                          2))
+        normalize_gt_keypoints = gt_keypoints_reshape[
+            ..., :2] / factor[:, :2].unsqueeze(0)
+        kpt_cost = self.kpt_cost(kpt_pred_tmp, normalize_gt_keypoints,
+                                 valid_kpt_flag)
+        # keypoint OKS cost
+        kpt_pred_tmp = kpt_pred.clone().detach().reshape((kpt_pred.shape[0], -1,
+                                                          2))
+        kpt_pred_tmp = kpt_pred_tmp * factor[:, :2].unsqueeze(0)
+        oks_cost = self.oks_cost(kpt_pred_tmp, gt_keypoints_reshape[..., :2],
+                                 valid_kpt_flag, gt_areas)
+        # weighted sum of above three costs
+        cost = cls_cost + kpt_cost + oks_cost
+
+        # 3. do Hungarian matching on CPU using linear_sum_assignment
+        cost = cost.detach().cpu()
+        if linear_sum_assignment is None:
+            raise ImportError('Please run "pip install scipy" '
+                              'to install scipy first.')
+        matched_row_inds, matched_col_inds = linear_sum_assignment(cost)
+        matched_row_inds = paddle.to_tensor(matched_row_inds)
+        matched_col_inds = paddle.to_tensor(matched_col_inds)
+
+        # 4. assign backgrounds and foregrounds
+        # assign all indices to backgrounds first
+        assigned_gt_inds[:] = 0
+        # assign foregrounds based on matching results
+        assigned_gt_inds[matched_row_inds] = matched_col_inds + 1
+        assigned_labels[matched_row_inds] = gt_labels[matched_col_inds][
+            ..., 0].astype("int64")
+        return AssignResult(
+            num_gts, assigned_gt_inds, None, labels=assigned_labels)
+
+
+class SamplingResult:
+    """Bbox sampling result.
+    """
+
+    def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result,
+                 gt_flags):
+        self.pos_inds = pos_inds
+        self.neg_inds = neg_inds
+        if pos_inds.size > 0:
+            self.pos_bboxes = bboxes[pos_inds]
+            self.neg_bboxes = bboxes[neg_inds]
+            self.pos_is_gt = gt_flags[pos_inds]
+
+            self.num_gts = gt_bboxes.shape[0]
+            self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1
+
+            if gt_bboxes.numel() == 0:
+                # hack for index error case
+                assert self.pos_assigned_gt_inds.numel() == 0
+                self.pos_gt_bboxes = paddle.zeros(
+                    gt_bboxes.shape, dtype=gt_bboxes.dtype).reshape((-1, 4))
+            else:
+                if len(gt_bboxes.shape) < 2:
+                    gt_bboxes = gt_bboxes.reshape((-1, 4))
+
+                self.pos_gt_bboxes = paddle.index_select(
+                    gt_bboxes,
+                    self.pos_assigned_gt_inds.astype('int64'),
+                    axis=0)
+
+            if assign_result.labels is not None:
+                self.pos_gt_labels = assign_result.labels[pos_inds]
+            else:
+                self.pos_gt_labels = None
+
+    @property
+    def bboxes(self):
+        """paddle.Tensor: concatenated positive and negative boxes"""
+        return paddle.concat([self.pos_bboxes, self.neg_bboxes])
+
+    def __nice__(self):
+        data = self.info.copy()
+        data['pos_bboxes'] = data.pop('pos_bboxes').shape
+        data['neg_bboxes'] = data.pop('neg_bboxes').shape
+        parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())]
+        body = '    ' + ',\n    '.join(parts)
+        return '{\n' + body + '\n}'
+
+    @property
+    def info(self):
+        """Returns a dictionary of info about the object."""
+        return {
+            'pos_inds': self.pos_inds,
+            'neg_inds': self.neg_inds,
+            'pos_bboxes': self.pos_bboxes,
+            'neg_bboxes': self.neg_bboxes,
+            'pos_is_gt': self.pos_is_gt,
+            'num_gts': self.num_gts,
+            'pos_assigned_gt_inds': self.pos_assigned_gt_inds,
+        }
+
+
+@register
+class PseudoSampler:
+    """A pseudo sampler that does not do sampling actually."""
+
+    def __init__(self, **kwargs):
+        pass
+
+    def _sample_pos(self, **kwargs):
+        """Sample positive samples."""
+        raise NotImplementedError
+
+    def _sample_neg(self, **kwargs):
+        """Sample negative samples."""
+        raise NotImplementedError
+
+    def sample(self, assign_result, bboxes, gt_bboxes, *args, **kwargs):
+        """Directly returns the positive and negative indices  of samples.
+
+        Args:
+            assign_result (:obj:`AssignResult`): Assigned results
+            bboxes (paddle.Tensor): Bounding boxes
+            gt_bboxes (paddle.Tensor): Ground truth boxes
+
+        Returns:
+            :obj:`SamplingResult`: sampler results
+        """
+        pos_inds = paddle.nonzero(
+            assign_result.gt_inds > 0, as_tuple=False).squeeze(-1)
+        neg_inds = paddle.nonzero(
+            assign_result.gt_inds == 0, as_tuple=False).squeeze(-1)
+        gt_flags = paddle.zeros([bboxes.shape[0]], dtype='int32')
+        sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes,
+                                         assign_result, gt_flags)
+        return sampling_result

ppdet/modeling/assigners/pose_utils.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 numpy as np
+import paddle
+import paddle.nn.functional as F
+
+from ppdet.core.workspace import register
+
+__all__ = ['KptL1Cost', 'OksCost', 'ClassificationCost']
+
+
+def masked_fill(x, mask, value):
+    y = paddle.full(x.shape, value, x.dtype)
+    return paddle.where(mask, y, x)
+
+
+@register
+class KptL1Cost(object):
+    """KptL1Cost.
+
+    this function based on: https://github.com/hikvision-research/opera/blob/main/opera/core/bbox/match_costs/match_cost.py
+
+    Args:
+        weight (int | float, optional): loss_weight.
+    """
+
+    def __init__(self, weight=1.0):
+        self.weight = weight
+
+    def __call__(self, kpt_pred, gt_keypoints, valid_kpt_flag):
+        """
+        Args:
+            kpt_pred (Tensor): Predicted keypoints with normalized coordinates
+                (x_{i}, y_{i}), which are all in range [0, 1]. Shape
+                [num_query, K, 2].
+            gt_keypoints (Tensor): Ground truth keypoints with normalized
+                coordinates (x_{i}, y_{i}). Shape [num_gt, K, 2].
+            valid_kpt_flag (Tensor): valid flag of ground truth keypoints.
+                Shape [num_gt, K].
+
+        Returns:
+            paddle.Tensor: kpt_cost value with weight.
+        """
+        kpt_cost = []
+        for i in range(len(gt_keypoints)):
+            if gt_keypoints[i].size == 0:
+                kpt_cost.append(kpt_pred.sum() * 0)
+            kpt_pred_tmp = kpt_pred.clone()
+            valid_flag = valid_kpt_flag[i] > 0
+            valid_flag_expand = valid_flag.unsqueeze(0).unsqueeze(-1).expand_as(
+                kpt_pred_tmp)
+            if not valid_flag_expand.all():
+                kpt_pred_tmp = masked_fill(kpt_pred_tmp, ~valid_flag_expand, 0)
+            cost = F.pairwise_distance(
+                kpt_pred_tmp.reshape((kpt_pred_tmp.shape[0], -1)),
+                gt_keypoints[i].reshape((-1, )).unsqueeze(0),
+                p=1,
+                keepdim=True)
+            avg_factor = paddle.clip(
+                valid_flag.astype('float32').sum() * 2, 1.0)
+            cost = cost / avg_factor
+            kpt_cost.append(cost)
+        kpt_cost = paddle.concat(kpt_cost, axis=1)
+        return kpt_cost * self.weight
+
+
+@register
+class OksCost(object):
+    """OksCost.
+
+    this function based on: https://github.com/hikvision-research/opera/blob/main/opera/core/bbox/match_costs/match_cost.py
+
+    Args:
+        num_keypoints (int): number of keypoints
+        weight (int | float, optional): loss_weight.
+    """
+
+    def __init__(self, num_keypoints=17, weight=1.0):
+        self.weight = weight
+        if num_keypoints == 17:
+            self.sigmas = np.array(
+                [
+                    .26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07,
+                    1.07, .87, .87, .89, .89
+                ],
+                dtype=np.float32) / 10.0
+        elif num_keypoints == 14:
+            self.sigmas = np.array(
+                [
+                    .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89,
+                    .89, .79, .79
+                ],
+                dtype=np.float32) / 10.0
+        else:
+            raise ValueError(f'Unsupported keypoints number {num_keypoints}')
+
+    def __call__(self, kpt_pred, gt_keypoints, valid_kpt_flag, gt_areas):
+        """
+        Args:
+            kpt_pred (Tensor): Predicted keypoints with unnormalized
+                coordinates (x_{i}, y_{i}). Shape [num_query, K, 2].
+            gt_keypoints (Tensor): Ground truth keypoints with unnormalized
+                coordinates (x_{i}, y_{i}). Shape [num_gt, K, 2].
+            valid_kpt_flag (Tensor): valid flag of ground truth keypoints.
+                Shape [num_gt, K].
+            gt_areas (Tensor): Ground truth mask areas. Shape [num_gt,].
+
+        Returns:
+            paddle.Tensor: oks_cost value with weight.
+        """
+        sigmas = paddle.to_tensor(self.sigmas)
+        variances = (sigmas * 2)**2
+
+        oks_cost = []
+        assert len(gt_keypoints) == len(gt_areas)
+        for i in range(len(gt_keypoints)):
+            if gt_keypoints[i].size == 0:
+                oks_cost.append(kpt_pred.sum() * 0)
+            squared_distance = \
+                (kpt_pred[:, :, 0] - gt_keypoints[i, :, 0].unsqueeze(0)) ** 2 + \
+                (kpt_pred[:, :, 1] - gt_keypoints[i, :, 1].unsqueeze(0)) ** 2
+            vis_flag = (valid_kpt_flag[i] > 0).astype('int')
+            vis_ind = vis_flag.nonzero(as_tuple=False)[:, 0]
+            num_vis_kpt = vis_ind.shape[0]
+            # assert num_vis_kpt > 0
+            if num_vis_kpt == 0:
+                oks_cost.append(paddle.zeros((squared_distance.shape[0], 1)))
+                continue
+            area = gt_areas[i]
+
+            squared_distance0 = squared_distance / (area * variances * 2)
+            squared_distance0 = paddle.index_select(
+                squared_distance0, vis_ind, axis=1)
+            squared_distance1 = paddle.exp(-squared_distance0).sum(axis=1,
+                                                                   keepdim=True)
+            oks = squared_distance1 / num_vis_kpt
+            # The 1 is a constant that doesn't change the matching, so omitted.
+            oks_cost.append(-oks)
+        oks_cost = paddle.concat(oks_cost, axis=1)
+        return oks_cost * self.weight
+
+
+@register
+class ClassificationCost:
+    """ClsSoftmaxCost.
+
+     Args:
+         weight (int | float, optional): loss_weight
+    """
+
+    def __init__(self, weight=1.):
+        self.weight = weight
+
+    def __call__(self, cls_pred, gt_labels):
+        """
+        Args:
+            cls_pred (Tensor): Predicted classification logits, shape
+                (num_query, num_class).
+            gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,).
+
+        Returns:
+            paddle.Tensor: cls_cost value with weight
+        """
+        # Following the official DETR repo, contrary to the loss that
+        # NLL is used, we approximate it in 1 - cls_score[gt_label].
+        # The 1 is a constant that doesn't change the matching,
+        # so it can be omitted.
+        cls_score = cls_pred.softmax(-1)
+        cls_cost = -cls_score[:, gt_labels]
+        return cls_cost * self.weight
+
+
+@register
+class FocalLossCost:
+    """FocalLossCost.
+
+     Args:
+         weight (int | float, optional): loss_weight
+         alpha (int | float, optional): focal_loss alpha
+         gamma (int | float, optional): focal_loss gamma
+         eps (float, optional): default 1e-12
+         binary_input (bool, optional): Whether the input is binary,
+            default False.
+    """
+
+    def __init__(self,
+                 weight=1.,
+                 alpha=0.25,
+                 gamma=2,
+                 eps=1e-12,
+                 binary_input=False):
+        self.weight = weight
+        self.alpha = alpha
+        self.gamma = gamma
+        self.eps = eps
+        self.binary_input = binary_input
+
+    def _focal_loss_cost(self, cls_pred, gt_labels):
+        """
+        Args:
+            cls_pred (Tensor): Predicted classification logits, shape
+                (num_query, num_class).
+            gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,).
+
+        Returns:
+            paddle.Tensor: cls_cost value with weight
+        """
+        if gt_labels.size == 0:
+            return cls_pred.sum() * 0
+        cls_pred = F.sigmoid(cls_pred)
+        neg_cost = -(1 - cls_pred + self.eps).log() * (
+            1 - self.alpha) * cls_pred.pow(self.gamma)
+        pos_cost = -(cls_pred + self.eps).log() * self.alpha * (
+            1 - cls_pred).pow(self.gamma)
+
+        cls_cost = paddle.index_select(
+            pos_cost, gt_labels, axis=1) - paddle.index_select(
+                neg_cost, gt_labels, axis=1)
+        return cls_cost * self.weight
+
+    def _mask_focal_loss_cost(self, cls_pred, gt_labels):
+        """
+        Args:
+            cls_pred (Tensor): Predicted classfication logits
+                in shape (num_query, d1, ..., dn), dtype=paddle.float32.
+            gt_labels (Tensor): Ground truth in shape (num_gt, d1, ..., dn),
+                dtype=paddle.long. Labels should be binary.
+
+        Returns:
+            Tensor: Focal cost matrix with weight in shape\
+                (num_query, num_gt).
+        """
+        cls_pred = cls_pred.flatten(1)
+        gt_labels = gt_labels.flatten(1).float()
+        n = cls_pred.shape[1]
+        cls_pred = F.sigmoid(cls_pred)
+        neg_cost = -(1 - cls_pred + self.eps).log() * (
+            1 - self.alpha) * cls_pred.pow(self.gamma)
+        pos_cost = -(cls_pred + self.eps).log() * self.alpha * (
+            1 - cls_pred).pow(self.gamma)
+
+        cls_cost = paddle.einsum('nc,mc->nm', pos_cost, gt_labels) + \
+            paddle.einsum('nc,mc->nm', neg_cost, (1 - gt_labels))
+        return cls_cost / n * self.weight
+
+    def __call__(self, cls_pred, gt_labels):
+        """
+        Args:
+            cls_pred (Tensor): Predicted classfication logits.
+            gt_labels (Tensor)): Labels.
+
+        Returns:
+            Tensor: Focal cost matrix with weight in shape\
+                (num_query, num_gt).
+        """
+        if self.binary_input:
+            return self._mask_focal_loss_cost(cls_pred, gt_labels)
+        else:
+            return self._focal_loss_cost(cls_pred, gt_labels)

ppdet/modeling/backbones/resnet.py

@@ -285,36 +285,6 @@ class BottleNeck(nn.Layer):
         # ResNeXt
         width = int(ch_out * (base_width / 64.)) * groups
 
-        self.shortcut = shortcut
-        if not shortcut:
-            if variant == 'd' and stride == 2:
-                self.short = nn.Sequential()
-                self.short.add_sublayer(
-                    'pool',
-                    nn.AvgPool2D(
-                        kernel_size=2, stride=2, padding=0, ceil_mode=True))
-                self.short.add_sublayer(
-                    'conv',
-                    ConvNormLayer(
-                        ch_in=ch_in,
-                        ch_out=ch_out * self.expansion,
-                        filter_size=1,
-                        stride=1,
-                        norm_type=norm_type,
-                        norm_decay=norm_decay,
-                        freeze_norm=freeze_norm,
-                        lr=lr))
-            else:
-                self.short = ConvNormLayer(
-                    ch_in=ch_in,
-                    ch_out=ch_out * self.expansion,
-                    filter_size=1,
-                    stride=stride,
-                    norm_type=norm_type,
-                    norm_decay=norm_decay,
-                    freeze_norm=freeze_norm,
-                    lr=lr)
-
         self.branch2a = ConvNormLayer(
             ch_in=ch_in,
             ch_out=width,
@@ -351,6 +321,36 @@ class BottleNeck(nn.Layer):
             freeze_norm=freeze_norm,
             lr=lr)
 
+        self.shortcut = shortcut
+        if not shortcut:
+            if variant == 'd' and stride == 2:
+                self.short = nn.Sequential()
+                self.short.add_sublayer(
+                    'pool',
+                    nn.AvgPool2D(
+                        kernel_size=2, stride=2, padding=0, ceil_mode=True))
+                self.short.add_sublayer(
+                    'conv',
+                    ConvNormLayer(
+                        ch_in=ch_in,
+                        ch_out=ch_out * self.expansion,
+                        filter_size=1,
+                        stride=1,
+                        norm_type=norm_type,
+                        norm_decay=norm_decay,
+                        freeze_norm=freeze_norm,
+                        lr=lr))
+            else:
+                self.short = ConvNormLayer(
+                    ch_in=ch_in,
+                    ch_out=ch_out * self.expansion,
+                    filter_size=1,
+                    stride=stride,
+                    norm_type=norm_type,
+                    norm_decay=norm_decay,
+                    freeze_norm=freeze_norm,
+                    lr=lr)
+
         self.std_senet = std_senet
         if self.std_senet:
             self.se = SELayer(ch_out * self.expansion)

ppdet/modeling/backbones/vision_transformer.py

@@ -284,9 +284,9 @@ class RelativePositionBias(nn.Layer):
 
     def forward(self):
         relative_position_bias = \
-            self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.relative_position_bias_table[self.relative_position_index.reshape([-1])].reshape([
                  self.window_size[0] * self.window_size[1] + 1,
-                 self.window_size[0] * self.window_size[1] + 1, -1)  # Wh*Ww,Wh*Ww,nH 
+                 self.window_size[0] * self.window_size[1] + 1, -1])  # Wh*Ww,Wh*Ww,nH 
         return relative_position_bias.transpose((2, 0, 1))  # nH, Wh*Ww, Wh*Ww
 
 

ppdet/modeling/heads/__init__.py

@@ -67,3 +67,4 @@ from .yolof_head import *
 from .ppyoloe_contrast_head import *
 from .centertrack_head import *
 from .sparse_roi_head import *
+from .petr_head import *

ppdet/modeling/layers.py

@@ -1135,7 +1135,7 @@ def _convert_attention_mask(attn_mask, dtype):
     """
     return nn.layer.transformer._convert_attention_mask(attn_mask, dtype)
 
-
+@register
 class MultiHeadAttention(nn.Layer):
     """
     Attention mapps queries and a set of key-value pairs to outputs, and

ppdet/modeling/losses/focal_loss.py

@@ -21,7 +21,7 @@ import paddle.nn.functional as F
 import paddle.nn as nn
 from ppdet.core.workspace import register
 
-__all__ = ['FocalLoss']
+__all__ = ['FocalLoss', 'Weighted_FocalLoss']
 
 @register
 class FocalLoss(nn.Layer):
@@ -59,3 +59,80 @@ class FocalLoss(nn.Layer):
             pred, target, alpha=self.alpha, gamma=self.gamma,
             reduction=reduction)
         return loss * self.loss_weight
+
+
+@register
+class Weighted_FocalLoss(FocalLoss):
+    """A wrapper around paddle.nn.functional.sigmoid_focal_loss.
+    Args:
+        use_sigmoid (bool): currently only support use_sigmoid=True
+        alpha (float): parameter alpha in Focal Loss
+        gamma (float): parameter gamma in Focal Loss
+        loss_weight (float): final loss will be multiplied by this
+    """
+    def __init__(self,
+                 use_sigmoid=True,
+                 alpha=0.25,
+                 gamma=2.0,
+                 loss_weight=1.0,
+                 reduction="mean"):
+        super(FocalLoss, self).__init__()
+        assert use_sigmoid == True, \
+            'Focal Loss only supports sigmoid at the moment'
+        self.use_sigmoid = use_sigmoid
+        self.alpha = alpha
+        self.gamma = gamma
+        self.loss_weight = loss_weight
+        self.reduction = reduction
+
+    def forward(self, pred, target, weight=None, avg_factor=None, reduction_override=None):
+        """forward function.
+        Args:
+            pred (Tensor): logits of class prediction, of shape (N, num_classes)
+            target (Tensor): target class label, of shape (N, )
+            reduction (str): the way to reduce loss, one of (none, sum, mean)
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        num_classes = pred.shape[1]
+        target = F.one_hot(target, num_classes + 1).astype(pred.dtype)
+        target = target[:, :-1].detach()
+        loss = F.sigmoid_focal_loss(
+            pred, target, alpha=self.alpha, gamma=self.gamma,
+            reduction='none')
+
+        if weight is not None:
+            if weight.shape != loss.shape:
+                if weight.shape[0] == loss.shape[0]:
+                    # For most cases, weight is of shape (num_priors, ),
+                    #  which means it does not have the second axis num_class
+                    weight = weight.reshape((-1, 1))
+                else:
+                    # Sometimes, weight per anchor per class is also needed. e.g.
+                    #  in FSAF. But it may be flattened of shape
+                    #  (num_priors x num_class, ), while loss is still of shape
+                    #  (num_priors, num_class).
+                    assert weight.numel() == loss.numel()
+                    weight = weight.reshape((loss.shape[0], -1))
+            assert weight.ndim == loss.ndim
+            loss = loss * weight
+
+        # if avg_factor is not specified, just reduce the loss
+        if avg_factor is None:
+            if reduction == 'mean':
+                loss = loss.mean()
+            elif reduction == 'sum':
+                loss = loss.sum()
+        else:
+            # if reduction is mean, then average the loss by avg_factor
+            if reduction == 'mean':
+                # Avoid causing ZeroDivisionError when avg_factor is 0.0,
+                # i.e., all labels of an image belong to ignore index.
+                eps = 1e-10
+                loss = loss.sum() / (avg_factor + eps)
+            # if reduction is 'none', then do nothing, otherwise raise an error
+            elif reduction != 'none':
+                raise ValueError('avg_factor can not be used with reduction="sum"')
+
+        return loss * self.loss_weight

ppdet/modeling/losses/keypoint_loss.py

@@ -18,12 +18,13 @@ from __future__ import print_function
 
 from itertools import cycle, islice
 from collections import abc
+import numpy as np
 import paddle
 import paddle.nn as nn
 
 from ppdet.core.workspace import register, serializable
 
-__all__ = ['HrHRNetLoss', 'KeyPointMSELoss']
+__all__ = ['HrHRNetLoss', 'KeyPointMSELoss', 'OKSLoss', 'CenterFocalLoss', 'L1Loss']
 
 
 @register
@@ -226,3 +227,406 @@ def recursive_sum(inputs):
     if isinstance(inputs, abc.Sequence):
         return sum([recursive_sum(x) for x in inputs])
     return inputs
+
+
+def oks_overlaps(kpt_preds, kpt_gts, kpt_valids, kpt_areas, sigmas):
+    if not kpt_gts.astype('bool').any():
+        return kpt_preds.sum()*0
+    
+    sigmas = paddle.to_tensor(sigmas, dtype=kpt_preds.dtype)
+    variances = (sigmas * 2)**2
+
+    assert kpt_preds.shape[0] == kpt_gts.shape[0]
+    kpt_preds = kpt_preds.reshape((-1, kpt_preds.shape[-1] // 2, 2))
+    kpt_gts = kpt_gts.reshape((-1, kpt_gts.shape[-1] // 2, 2))
+
+    squared_distance = (kpt_preds[:, :, 0] - kpt_gts[:, :, 0]) ** 2 + \
+        (kpt_preds[:, :, 1] - kpt_gts[:, :, 1]) ** 2
+    assert (kpt_valids.sum(-1) > 0).all()
+    squared_distance0 = squared_distance / (
+        kpt_areas[:, None] * variances[None, :] * 2)
+    squared_distance1 = paddle.exp(-squared_distance0)
+    squared_distance1 = squared_distance1 * kpt_valids
+    oks = squared_distance1.sum(axis=1) / kpt_valids.sum(axis=1)
+
+    return oks
+
+
+def oks_loss(pred,
+             target,
+             weight,
+             valid=None,
+             area=None,
+             linear=False,
+             sigmas=None,
+             eps=1e-6,
+             avg_factor=None, 
+             reduction=None):
+    """Oks loss.
+
+    Computing the oks loss between a set of predicted poses and target poses.
+    The loss is calculated as negative log of oks.
+
+    Args:
+        pred (Tensor): Predicted poses of format (x1, y1, x2, y2, ...),
+            shape (n, K*2).
+        target (Tensor): Corresponding gt poses, shape (n, K*2).
+        linear (bool, optional): If True, use linear scale of loss instead of
+            log scale. Default: False.
+        eps (float): Eps to avoid log(0).
+
+    Returns:
+        Tensor: Loss tensor.
+    """
+    oks = oks_overlaps(pred, target, valid, area, sigmas).clip(min=eps)
+    if linear:
+        loss = 1 - oks
+    else:
+        loss = -oks.log()
+
+    if weight is not None:
+        if weight.shape != loss.shape:
+            if weight.shape[0] == loss.shape[0]:
+                # For most cases, weight is of shape (num_priors, ),
+                #  which means it does not have the second axis num_class
+                weight = weight.reshape((-1, 1))
+            else:
+                # Sometimes, weight per anchor per class is also needed. e.g.
+                #  in FSAF. But it may be flattened of shape
+                #  (num_priors x num_class, ), while loss is still of shape
+                #  (num_priors, num_class).
+                assert weight.numel() == loss.numel()
+                weight = weight.reshape((loss.shape[0], -1))
+        assert weight.ndim == loss.ndim
+        loss = loss * weight
+
+    # if avg_factor is not specified, just reduce the loss
+    if avg_factor is None:
+        if reduction == 'mean':
+            loss = loss.mean()
+        elif reduction == 'sum':
+            loss = loss.sum()
+    else:
+        # if reduction is mean, then average the loss by avg_factor
+        if reduction == 'mean':
+            # Avoid causing ZeroDivisionError when avg_factor is 0.0,
+            # i.e., all labels of an image belong to ignore index.
+            eps = 1e-10
+            loss = loss.sum() / (avg_factor + eps)
+        # if reduction is 'none', then do nothing, otherwise raise an error
+        elif reduction != 'none':
+            raise ValueError('avg_factor can not be used with reduction="sum"')
+
+
+    return loss
+
+@register
+@serializable
+class OKSLoss(nn.Layer):
+    """OKSLoss.
+
+    Computing the oks loss between a set of predicted poses and target poses.
+
+    Args:
+        linear (bool): If True, use linear scale of loss instead of log scale.
+            Default: False.
+        eps (float): Eps to avoid log(0).
+        reduction (str): Options are "none", "mean" and "sum".
+        loss_weight (float): Weight of loss.
+    """
+
+    def __init__(self,
+                 linear=False,
+                 num_keypoints=17,
+                 eps=1e-6,
+                 reduction='mean',
+                 loss_weight=1.0):
+        super(OKSLoss, self).__init__()
+        self.linear = linear
+        self.eps = eps
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+        if num_keypoints == 17:
+            self.sigmas = np.array([
+                .26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07,
+                1.07, .87, .87, .89, .89
+            ], dtype=np.float32) / 10.0
+        elif num_keypoints == 14:
+            self.sigmas = np.array([
+                .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89,
+                .79, .79
+            ]) / 10.0
+        else:
+            raise ValueError(f'Unsupported keypoints number {num_keypoints}')
+
+    def forward(self,
+                pred,
+                target,
+                valid,
+                area,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None,
+                **kwargs):
+        """Forward function.
+
+        Args:
+            pred (Tensor): The prediction.
+            target (Tensor): The learning target of the prediction.
+            valid (Tensor): The visible flag of the target pose.
+            area (Tensor): The area of the target pose.
+            weight (Tensor, optional): The weight of loss for each
+                prediction. Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Defaults to None. Options are "none", "mean" and "sum".
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        if (weight is not None) and (not paddle.any(weight > 0)) and (
+                reduction != 'none'):
+            if pred.dim() == weight.dim() + 1:
+                weight = weight.unsqueeze(1)
+            return (pred * weight).sum()  # 0
+        if weight is not None and weight.dim() > 1:
+            # TODO: remove this in the future
+            # reduce the weight of shape (n, 4) to (n,) to match the
+            # iou_loss of shape (n,)
+            assert weight.shape == pred.shape
+            weight = weight.mean(-1)
+        loss = self.loss_weight * oks_loss(
+            pred,
+            target,
+            weight,
+            valid=valid,
+            area=area,
+            linear=self.linear,
+            sigmas=self.sigmas,
+            eps=self.eps,
+            reduction=reduction,
+            avg_factor=avg_factor,
+            **kwargs)
+        return loss
+
+
+def center_focal_loss(pred, gt, weight=None, mask=None, avg_factor=None, reduction=None):
+    """Modified focal loss. Exactly the same as CornerNet.
+    Runs faster and costs a little bit more memory.
+
+    Args:
+        pred (Tensor): The prediction with shape [bs, c, h, w].
+        gt (Tensor): The learning target of the prediction in gaussian
+            distribution, with shape [bs, c, h, w].
+        mask (Tensor): The valid mask. Defaults to None.
+    """
+    if not gt.astype('bool').any():
+        return pred.sum()*0
+    pos_inds = gt.equal(1).astype('float32')
+    if mask is None:
+        neg_inds = gt.less_than(paddle.to_tensor([1], dtype='float32')).astype('float32')
+    else:
+        neg_inds = gt.less_than(paddle.to_tensor([1], dtype='float32')).astype('float32') * mask.equal(0).astype('float32')
+
+    neg_weights = paddle.pow(1 - gt, 4)
+
+    loss = 0
+
+    pos_loss = paddle.log(pred) * paddle.pow(1 - pred, 2) * pos_inds
+    neg_loss = paddle.log(1 - pred) * paddle.pow(pred, 2) * neg_weights * \
+        neg_inds
+
+    num_pos = pos_inds.astype('float32').sum()
+    pos_loss = pos_loss.sum()
+    neg_loss = neg_loss.sum()
+
+    if num_pos == 0:
+        loss = loss - neg_loss
+    else:
+        loss = loss - (pos_loss + neg_loss) / num_pos
+
+    if weight is not None:
+        if weight.shape != loss.shape:
+            if weight.shape[0] == loss.shape[0]:
+                # For most cases, weight is of shape (num_priors, ),
+                #  which means it does not have the second axis num_class
+                weight = weight.reshape((-1, 1))
+            else:
+                # Sometimes, weight per anchor per class is also needed. e.g.
+                #  in FSAF. But it may be flattened of shape
+                #  (num_priors x num_class, ), while loss is still of shape
+                #  (num_priors, num_class).
+                assert weight.numel() == loss.numel()
+                weight = weight.reshape((loss.shape[0], -1))
+        assert weight.ndim == loss.ndim
+        loss = loss * weight
+
+    # if avg_factor is not specified, just reduce the loss
+    if avg_factor is None:
+        if reduction == 'mean':
+            loss = loss.mean()
+        elif reduction == 'sum':
+            loss = loss.sum()
+    else:
+        # if reduction is mean, then average the loss by avg_factor
+        if reduction == 'mean':
+            # Avoid causing ZeroDivisionError when avg_factor is 0.0,
+            # i.e., all labels of an image belong to ignore index.
+            eps = 1e-10
+            loss = loss.sum() / (avg_factor + eps)
+        # if reduction is 'none', then do nothing, otherwise raise an error
+        elif reduction != 'none':
+            raise ValueError('avg_factor can not be used with reduction="sum"')
+
+    return loss
+
+@register
+@serializable
+class CenterFocalLoss(nn.Layer):
+    """CenterFocalLoss is a variant of focal loss.
+
+    More details can be found in the `paper
+    <https://arxiv.org/abs/1808.01244>`_
+
+    Args:
+        reduction (str): Options are "none", "mean" and "sum".
+        loss_weight (float): Loss weight of current loss.
+    """
+
+    def __init__(self,
+                 reduction='none',
+                 loss_weight=1.0):
+        super(CenterFocalLoss, self).__init__()
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                mask=None,
+                avg_factor=None,
+                reduction_override=None):
+        """Forward function.
+
+        Args:
+            pred (Tensor): The prediction.
+            target (Tensor): The learning target of the prediction in gaussian
+                distribution.
+            weight (Tensor, optional): The weight of loss for each
+                prediction. Defaults to None.
+            mask (Tensor): The valid mask. Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Defaults to None.
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        loss_reg = self.loss_weight * center_focal_loss(
+            pred,
+            target,
+            weight,
+            mask=mask,
+            reduction=reduction,
+            avg_factor=avg_factor)
+        return loss_reg
+
+def l1_loss(pred, target, weight=None, reduction='mean', avg_factor=None):
+    """L1 loss.
+
+    Args:
+        pred (Tensor): The prediction.
+        target (Tensor): The learning target of the prediction.
+
+    Returns:
+        Tensor: Calculated loss
+    """
+    if not target.astype('bool').any():
+        return pred.sum() * 0
+
+    assert pred.shape == target.shape
+    loss = paddle.abs(pred - target)
+
+    if weight is not None:
+        if weight.shape != loss.shape:
+            if weight.shape[0] == loss.shape[0]:
+                # For most cases, weight is of shape (num_priors, ),
+                #  which means it does not have the second axis num_class
+                weight = weight.reshape((-1, 1))
+            else:
+                # Sometimes, weight per anchor per class is also needed. e.g.
+                #  in FSAF. But it may be flattened of shape
+                #  (num_priors x num_class, ), while loss is still of shape
+                #  (num_priors, num_class).
+                assert weight.numel() == loss.numel()
+                weight = weight.reshape((loss.shape[0], -1))
+        assert weight.ndim == loss.ndim
+        loss = loss * weight
+
+    # if avg_factor is not specified, just reduce the loss
+    if avg_factor is None:
+        if reduction == 'mean':
+            loss = loss.mean()
+        elif reduction == 'sum':
+            loss = loss.sum()
+    else:
+        # if reduction is mean, then average the loss by avg_factor
+        if reduction == 'mean':
+            # Avoid causing ZeroDivisionError when avg_factor is 0.0,
+            # i.e., all labels of an image belong to ignore index.
+            eps = 1e-10
+            loss = loss.sum() / (avg_factor + eps)
+        # if reduction is 'none', then do nothing, otherwise raise an error
+        elif reduction != 'none':
+            raise ValueError('avg_factor can not be used with reduction="sum"')
+
+
+    return loss
+
+@register
+@serializable
+class L1Loss(nn.Layer):
+    """L1 loss.
+
+    Args:
+        reduction (str, optional): The method to reduce the loss.
+            Options are "none", "mean" and "sum".
+        loss_weight (float, optional): The weight of loss.
+    """
+
+    def __init__(self, reduction='mean', loss_weight=1.0):
+        super(L1Loss, self).__init__()
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None):
+        """Forward function.
+
+        Args:
+            pred (Tensor): The prediction.
+            target (Tensor): The learning target of the prediction.
+            weight (Tensor, optional): The weight of loss for each
+                prediction. Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Defaults to None.
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        loss_bbox = self.loss_weight * l1_loss(
+            pred, target, weight, reduction=reduction, avg_factor=avg_factor)
+        return loss_bbox
+

ppdet/modeling/necks/__init__.py

@@ -36,3 +36,4 @@ from .es_pan import *
 from .lc_pan import *
 from .custom_pan import *
 from .dilated_encoder import *
+from .channel_mapper import *

ppdet/modeling/necks/channel_mapper.py

+# Copyright (c) 2023 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.
+"""
+this code is base on mmdet: git@github.com:open-mmlab/mmdetection.git
+"""
+import paddle.nn as nn
+
+from ppdet.core.workspace import register, serializable
+from ..backbones.hrnet import ConvNormLayer
+from ..shape_spec import ShapeSpec
+from ..initializer import xavier_uniform_, constant_
+
+__all__ = ['ChannelMapper']
+
+
+@register
+@serializable
+class ChannelMapper(nn.Layer):
+    """Channel Mapper to reduce/increase channels of backbone features.
+
+    This is used to reduce/increase channels of backbone features.
+
+    Args:
+        in_channels (List[int]): Number of input channels per scale.
+        out_channels (int): Number of output channels (used at each scale).
+        kernel_size (int, optional): kernel_size for reducing channels (used
+            at each scale). Default: 3.
+        conv_cfg (dict, optional): Config dict for convolution layer.
+            Default: None.
+        norm_cfg (dict, optional): Config dict for normalization layer.
+            Default: None.
+        act_cfg (dict, optional): Config dict for activation layer in
+            ConvModule. Default: dict(type='ReLU').
+        num_outs (int, optional): Number of output feature maps. There
+            would be extra_convs when num_outs larger than the length
+            of in_channels.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+        
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=3,
+                 norm_type="gn",
+                 norm_groups=32,
+                 act='relu',
+                 num_outs=None,
+                 init_cfg=dict(
+                     type='Xavier', layer='Conv2d', distribution='uniform')):
+        super(ChannelMapper, self).__init__()
+        assert isinstance(in_channels, list)
+        self.extra_convs = None
+        if num_outs is None:
+            num_outs = len(in_channels)
+        self.convs = nn.LayerList()
+        for in_channel in in_channels:
+            self.convs.append(
+                ConvNormLayer(
+                    ch_in=in_channel,
+                    ch_out=out_channels,
+                    filter_size=kernel_size,
+                    norm_type='gn',
+                    norm_groups=32,
+                    act=act))
+
+        if num_outs > len(in_channels):
+            self.extra_convs = nn.LayerList()
+            for i in range(len(in_channels), num_outs):
+                if i == len(in_channels):
+                    in_channel = in_channels[-1]
+                else:
+                    in_channel = out_channels
+                self.extra_convs.append(
+                    ConvNormLayer(
+                        ch_in=in_channel,
+                        ch_out=out_channels,
+                        filter_size=3,
+                        stride=2,
+                        norm_type='gn',
+                        norm_groups=32,
+                        act=act))
+        self.init_weights()
+
+    def forward(self, inputs):
+        """Forward function."""
+        assert len(inputs) == len(self.convs)
+        outs = [self.convs[i](inputs[i]) for i in range(len(inputs))]
+        if self.extra_convs:
+            for i in range(len(self.extra_convs)):
+                if i == 0:
+                    outs.append(self.extra_convs[0](inputs[-1]))
+                else:
+                    outs.append(self.extra_convs[i](outs[-1]))
+        return tuple(outs)
+
+    @property
+    def out_shape(self):
+        return [
+            ShapeSpec(
+                channels=self.out_channel, stride=1. / s)
+            for s in self.spatial_scales
+        ]
+
+    def init_weights(self):
+        """Initialize the transformer weights."""
+        for p in self.parameters():
+            if p.rank() > 1:
+                xavier_uniform_(p)
+                if hasattr(p, 'bias') and p.bias is not None:
+                    constant_(p.bais)

ppdet/modeling/transformers/__init__.py

@@ -25,3 +25,4 @@ from .matchers import *
 from .position_encoding import *
 from .deformable_transformer import *
 from .dino_transformer import *
+from .petr_transformer import *

ppdet/utils/visualizer.py

@@ -238,7 +238,7 @@ def draw_pose(image,
                      'for example: `pip install matplotlib`.')
         raise e
 
-    skeletons = np.array([item['keypoints'] for item in results])
+    skeletons = np.array([item['keypoints'] for item in results]).reshape((-1, 51))
     kpt_nums = 17
     if len(skeletons) > 0:
         kpt_nums = int(skeletons.shape[1] / 3)