add swin focalnet backbone rtdetr models (#8309)

configs/rtdetr/README.md

@@ -2,8 +2,9 @@
 
 ## 最新动态
 
+- 发布RT-DETR-Swin和RT-DETR-FocalNet模型
 - 发布RT-DETR-R50和RT-DETR-R101的代码和预训练模型
-- 发布RT-DETR-L和RT-DETR-X的代码和预训练模型
+- **发布RT-DETR-L和RT-DETR-X的代码和预训练模型**
 - 发布RT-DETR-R50-m模型（scale模型的范例）
 - 发布RT-DETR-R34模型
 - 发布RT-DETR-R18模型
@@ -17,7 +18,7 @@ RT-DETR是第一个实时端到端目标检测器。具体而言，我们设计
   <img src="https://github.com/PaddlePaddle/PaddleDetection/assets/17582080/3184a08e-aa4d-49cf-9079-f3695c4cc1c3" width=500 />
 </div>
 
-## 模型
+## 基础模型
 
 | Model | Epoch | backbone  | input shape | $AP^{val}$ | $AP^{val}_{50}$| Params(M) | FLOPs(G) |  T4 TensorRT FP16(FPS) | Pretrained Model | config |
 |:--------------:|:-----:|:----------:| :-------:|:--------------------------:|:---------------------------:|:---------:|:--------:| :---------------------: |:------------------------------------------------------------------------------------:|:-------------------------------------------:|
@@ -29,10 +30,17 @@ RT-DETR是第一个实时端到端目标检测器。具体而言，我们设计
 | RT-DETR-L | 6x |  HGNetv2 | 640 | 53.0 | 71.6 | 32 | 110 | 114 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_hgnetv2_l_6x_coco.pdparams) | [config](rtdetr_hgnetv2_l_6x_coco.yml)
 | RT-DETR-X | 6x |  HGNetv2 | 640 | 54.8 | 73.1 | 67 | 234 | 74 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_hgnetv2_x_6x_coco.pdparams) | [config](rtdetr_hgnetv2_x_6x_coco.yml)
 
+## 高精度模型
+
+| Model | Epoch | backbone  | input shape | $AP^{val}$ | $AP^{val}_{50}$ | Pretrained Model | config |
+|:-----:|:-----:|:---------:| :---------:|:-----------:|:---------------:|:----------------:|:------:|
+| RT-DETR-Swin | 3x |  Swin_L_384 | 640 | 56.2 | 73.5 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_swin_L_384_3x_coco.pdparams) | [config](./rtdetr_swin_L_384_3x_coco.yml)
+| RT-DETR-FocalNet | 3x |  FocalNet_L_384  | 640 | 56.9 | 74.3 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_focalnet_L_384_3x_coco.pdparams) | [config](./rtdetr_focalnet_L_384_3x_coco.yml)
 
 **注意事项:**
-- RT-DETR 使用4个GPU训练。
+- RT-DETR 基础模型均使用4个GPU训练。
 - RT-DETR 在COCO train2017上训练，并在val2017上评估。
+- 高精度模型RT-DETR-Swin和RT-DETR-FocalNet使用8个GPU训练，显存需求较高。
 
 ## 快速开始
 

configs/rtdetr/rtdetr_focalnet_L_384_3x_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_6x.yml',
+  '_base_/rtdetr_r50vd.yml',
+  '_base_/rtdetr_reader.yml',
+]
+
+weights: output/rtdetr_focalnet_L_384_3x_coco/model_final
+find_unused_parameters: True
+log_iter: 100
+snapshot_epoch: 2
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/pretrained/focalnet_large_fl4_pretrained_on_o365.pdparams
+DETR:
+  backbone: FocalNet
+  neck: HybridEncoder
+  transformer: RTDETRTransformer
+  detr_head: DINOHead
+  post_process: DETRPostProcess
+
+FocalNet:
+  arch: 'focalnet_L_384_22k_fl4'
+  out_indices: [1, 2, 3]
+
+HybridEncoder:
+  hidden_dim: 256
+  use_encoder_idx: [2]
+  num_encoder_layers: 6 #
+  encoder_layer:
+    name: TransformerLayer
+    d_model: 256
+    nhead: 8
+    dim_feedforward: 2048
+    dropout: 0.
+    activation: 'gelu'
+  expansion: 1.0
+
+
+RTDETRTransformer:
+  num_queries: 300
+  position_embed_type: sine
+  feat_strides: [8, 16, 32]
+  num_levels: 3
+  nhead: 8
+  num_decoder_layers: 6
+  dim_feedforward: 2048 #
+  dropout: 0.0
+  activation: relu
+  num_denoising: 100
+  label_noise_ratio: 0.5
+  box_noise_scale: 1.0
+  learnt_init_query: False
+  query_pos_head_inv_sig: True #
+
+DINOHead:
+  loss:
+    name: DINOLoss
+    loss_coeff: {class: 1, bbox: 5, giou: 2}
+    aux_loss: True
+    use_vfl: True
+    matcher:
+      name: HungarianMatcher
+      matcher_coeff: {class: 2, bbox: 5, giou: 2}
+
+DETRPostProcess:
+  num_top_queries: 300
+
+
+epoch: 36
+LearningRate:
+  base_lr: 0.0001
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [36]
+    use_warmup: false
+
+OptimizerBuilder:
+  clip_grad_by_norm: 0.1
+  regularizer: false
+  optimizer:
+    type: AdamW
+    weight_decay: 0.0001
+    param_groups:
+      - params: ['absolute_pos_embed', 'relative_position_bias_table', 'norm']
+        weight_decay: 0.0

configs/rtdetr/rtdetr_swin_L_384_3x_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/optimizer_6x.yml',
+  '_base_/rtdetr_r50vd.yml',
+  '_base_/rtdetr_reader.yml',
+]
+
+weights: output/rtdetr_swin_L_384_3x_coco/model_final
+find_unused_parameters: True
+log_iter: 100
+snapshot_epoch: 2
+
+pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/dino_swin_large_384_4scale_3x_coco.pdparams
+DETR:
+  backbone: SwinTransformer
+  neck: HybridEncoder
+  transformer: RTDETRTransformer
+  detr_head: DINOHead
+  post_process: DETRPostProcess
+
+
+SwinTransformer:
+  arch: 'swin_L_384' # ['swin_T_224', 'swin_S_224', 'swin_B_224', 'swin_L_224', 'swin_B_384', 'swin_L_384']
+  ape: false
+  drop_path_rate: 0.2
+  patch_norm: true
+  out_indices: [1, 2, 3]
+
+HybridEncoder:
+  hidden_dim: 256
+  use_encoder_idx: [2]
+  num_encoder_layers: 6 #
+  encoder_layer:
+    name: TransformerLayer
+    d_model: 256
+    nhead: 8
+    dim_feedforward: 2048 #
+    dropout: 0.
+    activation: 'gelu'
+  expansion: 1.0
+
+RTDETRTransformer:
+  num_queries: 300
+  position_embed_type: sine
+  feat_strides: [8, 16, 32]
+  num_levels: 3
+  nhead: 8
+  num_decoder_layers: 6
+  dim_feedforward: 2048 #
+  dropout: 0.0
+  activation: relu
+  num_denoising: 100
+  label_noise_ratio: 0.5
+  box_noise_scale: 1.0
+  learnt_init_query: False
+
+DINOHead:
+  loss:
+    name: DINOLoss
+    loss_coeff: {class: 1, bbox: 5, giou: 2}
+    aux_loss: True
+    use_vfl: True
+    matcher:
+      name: HungarianMatcher
+      matcher_coeff: {class: 2, bbox: 5, giou: 2}
+
+DETRPostProcess:
+  num_top_queries: 300
+
+
+epoch: 36
+LearningRate:
+  base_lr: 0.0001
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [36]
+    use_warmup: false
+
+OptimizerBuilder:
+  clip_grad_by_norm: 0.1
+  regularizer: false
+  optimizer:
+    type: AdamW
+    weight_decay: 0.0001
+    param_groups:
+      - params: ['absolute_pos_embed', 'relative_position_bias_table', 'norm']
+        weight_decay: 0.0

ppdet/modeling/transformers/rtdetr_transformer.py

@@ -218,14 +218,19 @@ class TransformerDecoder(nn.Layer):
                 score_head,
                 query_pos_head,
                 attn_mask=None,
-                memory_mask=None):
+                memory_mask=None,
+                query_pos_head_inv_sig=False):
         output = tgt
         dec_out_bboxes = []
         dec_out_logits = []
         ref_points_detach = F.sigmoid(ref_points_unact)
         for i, layer in enumerate(self.layers):
             ref_points_input = ref_points_detach.unsqueeze(2)
-            query_pos_embed = query_pos_head(ref_points_detach)
+            if not query_pos_head_inv_sig:
+                query_pos_embed = query_pos_head(ref_points_detach)
+            else:
+                query_pos_embed = query_pos_head(
+                    inverse_sigmoid(ref_points_detach))
 
             output = layer(output, ref_points_input, memory,
                            memory_spatial_shapes, memory_level_start_index,
@@ -276,6 +281,7 @@ class RTDETRTransformer(nn.Layer):
                  label_noise_ratio=0.5,
                  box_noise_scale=1.0,
                  learnt_init_query=True,
+                 query_pos_head_inv_sig=False,
                  eval_size=None,
                  eval_idx=-1,
                  eps=1e-2):
@@ -321,6 +327,7 @@ class RTDETRTransformer(nn.Layer):
         if learnt_init_query:
             self.tgt_embed = nn.Embedding(num_queries, hidden_dim)
         self.query_pos_head = MLP(4, 2 * hidden_dim, hidden_dim, num_layers=2)
+        self.query_pos_head_inv_sig = query_pos_head_inv_sig
 
         # encoder head
         self.enc_output = nn.Sequential(
@@ -464,7 +471,9 @@ class RTDETRTransformer(nn.Layer):
             self.dec_bbox_head,
             self.dec_score_head,
             self.query_pos_head,
-            attn_mask=attn_mask)
+            attn_mask=attn_mask,
+            memory_mask=None,
+            query_pos_head_inv_sig=self.query_pos_head_inv_sig)
         return (out_bboxes, out_logits, enc_topk_bboxes, enc_topk_logits,
                 dn_meta)