add reparameterized version

Author: wondervictor

README.md

@@ -170,7 +170,19 @@ chmod +x tools/dist_test.sh
 
 ## Fine-tuning YOLO-World
 
-We provide the details about fine-tuning YOLO-World in [docs/fine-tuning](./docs/finetuning.md).
+<div align="center">
+<img src="./assets/finetune_yoloworld.png" width=800px>
+</div>
+
+
+YOLO-World supports **zero-shot inference**, and three types of **fine-tuning recipes**: **(1) normal fine-tuning**, **(2) prompt tuning**, and **(3) reparameterized fine-tuning**.
+
+
+* Normal Fine-tuning: we provide the details about fine-tuning YOLO-World in [docs/fine-tuning](./docs/finetuning.md).
+
+* Prompt Tuning: we provide more details ahout prompt tuning in [docs/prompt_yolo_world](./docs/prompt_yolo_world.md).
+
+* Reparameterized Fine-tuning: the reparameterized YOLO-World is more suitable for specific domains far from generic scenes. You can find more details in [`docs/reparameterize`](./docs/reparameterize.md).
 
 ## Deployment
 

assets/finetune_yoloworld.png

@@ -18,11 +18,13 @@ BTW, the COCO fine-tuning results are updated with higher performance (with `mas
 | [YOLO-World-v2-S](./yolo_world_v2_s_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py) | AdamW, 2e-4, 80e | ✔️  | ✖️ | 37.5 | 46.1 | 62.0 | 49.9 | [HF Checkpoints](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_s_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_ep80-492dc329.pth) | [log](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_s_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240327_110411.log) |
 | [YOLO-World-v2-M](./yolo_world_v2_m_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py) | AdamW, 2e-4, 80e | ✔️  | ✖️ | 42.8 | 51.0 | 67.5 | 55.2 | [HF Checkpoints](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_m_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_ep80-69c27ac7.pth) | [log](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_m_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240327_110411.log) |
 | [YOLO-World-v2-L](./yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py) | AdamW, 2e-4, 80e | ✔️  | ✖️ | 45.1 | 53.9 | 70.9 | 58.8 | [HF Checkpoints](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_ep80-81c701ee.pth) | [log](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240326_160313.log) |
-| [YOLO-World-v2-L](./yolo_world_v2_l_efficient_neck_2e-4_80e_8gpus_mask-refine_finetune_coco.py) | AdamW, 2e-4, 80e | ✔️  |  ✔️ | 45.1 | |  | | [HF Checkpoints]() | [log]() |
 | [YOLO-World-v2-X](./yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py) | AdamW, 2e-4, 80e | ✔️  | ✖️ | 46.8 | 54.7 | 71.6 | 59.6 | [HF Checkpoints](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_ep80-76bc0cbd.pth) | [log](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240322_181232.log) |
 | [YOLO-World-v2-L](./yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_finetune_coco.py) 🔥 | SGD, 1e-3, 40e | ✖️  | ✖️ | 45.1 | 52.8 | 69.5 | 57.8 | [HF Checkpoints](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_finetune_coco_ep80-e1288152.pth) | [log](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_finetuning_coco_20240327_014902.log) |
 
 
+### Reparameterized Training
 
 
-### Reparameterized Training
+| model | Schedule | `mask-refine` | efficient neck | AP<sup>ZS</sup>|  AP | AP<sub>50</sub> | AP<sub>75</sub> | weights | log |
+| :---- | :-------: | :----------: |:-------------: | :------------: | :-: | :--------------:| :-------------: |:------: | :-: |
+| [YOLO-World-v2-S](./yolo_world_v2_s_rep_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py) | AdamW, 2e-4, 80e | ✔️  | ✖️ | 37.5 | 46.3 | 62.8 | 50.4 | [HF Checkpoints]() | [log]() | 

assets/reparameterize.png

@@ -1,5 +1,5 @@
 _base_ = ('../../third_party/mmyolo/configs/yolov8/'
-          'yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco.py')
+          'yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco.py')
 custom_imports = dict(imports=['yolo_world'], allow_failed_imports=False)
 
 # hyper-parameters
@@ -11,11 +11,13 @@
 text_channels = 512
 neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
 neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
-base_lr = 2e-3
+base_lr = 2e-4
 weight_decay = 0.05
 train_batch_size_per_gpu = 16
-load_from = '/group/40034/adriancheng/notebooks/rep_models/yolo_world_v2_x_obj365v1_goldg_cc3mlite_pretrain_1280ft-14996a36_repconv.pth'
+load_from = '../FastDet/output_models/pretrain_yolow-v8_s_clipv2_frozen_te_noprompt_t2i_bn_2e-3adamw_scale_lr_wd_32xb16-100e_obj365v1_goldg_cc3mram250k_train_lviseval-e3592307_rep_conv.pth'
 persistent_workers = False
+mixup_prob = 0.15
+copypaste_prob = 0.3
 
 # model settings
 model = dict(type='SimpleYOLOWorldDetector',
@@ -28,14 +30,12 @@
                            type='MultiModalYOLOBackbone',
                            text_model=None,
                            image_model={{_base_.model.backbone}},
-                           frozen_stages=4,
                            with_text_model=False),
              neck=dict(type='YOLOWorldPAFPN',
-                       guide_channels=num_classes,
+                       guide_channels=text_channels,
                        embed_channels=neck_embed_channels,
                        num_heads=neck_num_heads,
-                       block_cfg=dict(type='RepConvMaxSigmoidCSPLayerWithTwoConv',
-                                      guide_channels=num_classes)),
+                       block_cfg=dict(type='EfficientCSPLayerWithTwoConv')),
              bbox_head=dict(head_module=dict(type='RepYOLOWorldHeadModule',
                                              embed_dims=text_channels,
                                              num_guide=num_classes,
@@ -53,7 +53,7 @@
          img_scale=_base_.img_scale,
          pad_val=114.0,
          pre_transform=_base_.pre_transform),
-    dict(type='YOLOv5CopyPaste', prob=_base_.copypaste_prob),
+    dict(type='YOLOv5CopyPaste', prob=copypaste_prob),
     dict(
         type='YOLOv5RandomAffine',
         max_rotate_degree=0.0,
@@ -69,7 +69,7 @@
 train_pipeline = [
     *_base_.pre_transform, *mosaic_affine_transform,
     dict(type='YOLOv5MixUp',
-         prob=_base_.mixup_prob,
+         prob=mixup_prob,
          pre_transform=[*_base_.pre_transform, *mosaic_affine_transform]),
     *_base_.last_transform[:-1], *final_transform
 ]
@@ -135,16 +135,6 @@
     lr=base_lr,
     weight_decay=weight_decay,
     batch_size_per_gpu=train_batch_size_per_gpu),
-                     paramwise_cfg=dict(bias_decay_mult=0.0,
-                                        norm_decay_mult=0.0,
-                                        custom_keys={
-                                            'backbone.text_model':
-                                            dict(lr_mult=0.01),
-                                            'logit_scale':
-                                            dict(weight_decay=0.0),
-                                            'embeddings':
-                                            dict(weight_decay=0.0)
-                                        }),
                      constructor='YOLOWv5OptimizerConstructor')
 
 # evaluation settings
@@ -153,4 +143,3 @@
                      proposal_nums=(100, 1, 10),
                      ann_file='data/coco/annotations/instances_val2017.json',
                      metric='bbox')
-find_unused_parameters = True

configs/finetune_coco/README.md

@@ -11,7 +11,7 @@
 text_channels = 512
 neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
 neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
-base_lr = 1e-3
+base_lr = 2e-3
 weight_decay = 0.0005
 train_batch_size_per_gpu = 16
 load_from = '../FastDet/output_models/yolo_world_s_clip_t2i_bn_2e-3adamw_32xb16-100e_obj365v1_goldg_train-55b943ea_rep_conv.pth'

configs/finetune_coco/yolo_world_v2_l_rep_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py configs/finetune_coco/yolo_world_v2_s_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py

@@ -11,7 +11,7 @@
 text_channels = 512
 neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
 neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
-base_lr = 1e-3
+base_lr = 5e-4
 weight_decay = 0.0005
 train_batch_size_per_gpu = 16
 load_from = '../FastDet/output_models/yolo_world_s_clip_t2i_bn_2e-3adamw_32xb16-100e_obj365v1_goldg_train-55b943ea_rep_conv.pth'
@@ -32,11 +32,10 @@
                            image_model={{_base_.model.backbone}},
                            with_text_model=False),
              neck=dict(type='YOLOWorldPAFPN',
-                       guide_channels=num_classes,
+                       guide_channels=text_channels,
                        embed_channels=neck_embed_channels,
                        num_heads=neck_num_heads,
-                       block_cfg=dict(type='RepConvMaxSigmoidCSPLayerWithTwoConv',
-                                      guide_channels=num_classes)),
+                       block_cfg=dict(type='EfficientCSPLayerWithTwoConv')),
              bbox_head=dict(head_module=dict(type='RepYOLOWorldHeadModule',
                                              embed_dims=text_channels,
                                              num_guide=num_classes,
@@ -140,7 +139,6 @@
     batch_size_per_gpu=train_batch_size_per_gpu),
                      constructor='YOLOWv5OptimizerConstructor')
 
-
 # evaluation settings
 val_evaluator = dict(_delete_=True,
                      type='mmdet.CocoMetric',

configs/finetune_coco/yolo_world_v2_s_rep_efficient_vlpan_sgd_1e-3_80e_8gpus_mask-refine_finetune_coco.py configs/finetune_coco/yolo_world_v2_s_rep_efficient_vlpan_sgd_2e-3_80e_8gpus_mask-refine_finetune_coco.py

@@ -1,6 +1,9 @@
-_base_ = ('../../third_party/mmyolo/configs/yolov8/'
-          'yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco.py')
-custom_imports = dict(imports=['yolo_world'], allow_failed_imports=False)
+_base_ = (
+    '../../third_party/mmyolo/configs/yolov8/'
+    'yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco.py')
+custom_imports = dict(
+    imports=['yolo_world'],
+    allow_failed_imports=False)
 
 # hyper-parameters
 num_classes = 80
@@ -11,12 +14,13 @@
 text_channels = 512
 neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2]
 neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32]
-base_lr = 2e-3
+base_lr = 2e-4
 weight_decay = 0.05
 train_batch_size_per_gpu = 16
-load_from = '/group/40034/adriancheng/notebooks/rep_models/yolo_world_v2_x_obj365v1_goldg_cc3mlite_pretrain_1280ft-14996a36_repconv.pth'
+load_from = '../YOLOWorld_Master/yolo_models/'
 persistent_workers = False
 
+
 # model settings
 model = dict(type='SimpleYOLOWorldDetector',
              mm_neck=True,
@@ -28,14 +32,12 @@
                            type='MultiModalYOLOBackbone',
                            text_model=None,
                            image_model={{_base_.model.backbone}},
-                           frozen_stages=4,
                            with_text_model=False),
              neck=dict(type='YOLOWorldPAFPN',
-                       guide_channels=num_classes,
+                       guide_channels=text_channels,
                        embed_channels=neck_embed_channels,
                        num_heads=neck_num_heads,
-                       block_cfg=dict(type='RepConvMaxSigmoidCSPLayerWithTwoConv',
-                                      guide_channels=num_classes)),
+                       block_cfg=dict(type='EfficientCSPLayerWithTwoConv')),
              bbox_head=dict(head_module=dict(type='RepYOLOWorldHeadModule',
                                              embed_dims=text_channels,
                                              num_guide=num_classes,
@@ -135,16 +137,6 @@
     lr=base_lr,
     weight_decay=weight_decay,
     batch_size_per_gpu=train_batch_size_per_gpu),
-                     paramwise_cfg=dict(bias_decay_mult=0.0,
-                                        norm_decay_mult=0.0,
-                                        custom_keys={
-                                            'backbone.text_model':
-                                            dict(lr_mult=0.01),
-                                            'logit_scale':
-                                            dict(weight_decay=0.0),
-                                            'embeddings':
-                                            dict(weight_decay=0.0)
-                                        }),
                      constructor='YOLOWv5OptimizerConstructor')
 
 # evaluation settings
@@ -153,4 +145,3 @@
                      proposal_nums=(100, 1, 10),
                      ann_file='data/coco/annotations/instances_val2017.json',
                      metric='bbox')
-find_unused_parameters = True

configs/finetune_coco/yolo_world_v2_s_rep_vlpan_bn_sgd_1e-3_80e_8gpus_mask-refine_finetune_coco.py configs/finetune_coco/yolo_world_v2_s_rep_efficient_vlpan_sgd_5e-4_80e_8gpus_mask-refine_finetune_coco.py

@@ -28,12 +28,14 @@ def __init__(self,
                  baseModel: nn.Module,
                  backend: MMYOLOBackend,
                  postprocess_cfg: Optional[ConfigDict] = None,
-                 with_nms=True):
+                 with_nms=True,
+                 without_bbox_decoder=False):
         super().__init__()
         self.baseModel = baseModel
         self.baseHead = baseModel.bbox_head
         self.backend = backend
         self.with_nms = with_nms
+        self.without_bbox_decoder = without_bbox_decoder
         if postprocess_cfg is None:
             self.with_postprocess = False
         else:
@@ -103,7 +105,8 @@ def pred_by_feat(self,
             bbox_decoder = yolox_bbox_decoder
         else:
             bbox_decoder = self.bbox_decoder
-
+        print(bbox_decoder)
+        
         num_imgs = cls_scores[0].shape[0]
         featmap_sizes = [cls_score.shape[2:] for cls_score in cls_scores]
 
@@ -112,7 +115,6 @@ def pred_by_feat(self,
                                           device=device)
 
         flatten_priors = torch.cat(mlvl_priors)
-
         mlvl_strides = [
             flatten_priors.new_full(
                 (featmap_size[0] * featmap_size[1] * self.num_base_priors, ),
@@ -121,8 +123,6 @@ def pred_by_feat(self,
         ]
         flatten_stride = torch.cat(mlvl_strides)
 
-        # flatten cls_scores, bbox_preds and objectness
-        # using score.shape
         text_len = cls_scores[0].shape[1]
         flatten_cls_scores = [
             cls_score.permute(0, 2, 3, 1).reshape(num_imgs, -1, text_len)
@@ -145,7 +145,9 @@ def pred_by_feat(self,
             cls_scores = cls_scores * (flatten_objectness.unsqueeze(-1))
 
         scores = cls_scores
-
+        bboxes = flatten_bbox_preds
+        if self.without_bbox_decoder:
+            return scores, bboxes
         bboxes = bbox_decoder(flatten_priors[None], flatten_bbox_preds,
                               flatten_stride)
 

configs/finetune_coco/yolo_world_v2_x_rep_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py configs/finetune_coco/yolo_world_v2_x_rep_efficient_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py

@@ -37,6 +37,9 @@ def parse_args():
     parser.add_argument('--without-nms',
                         action='store_true',
                         help='Expore model without NMS')
+    parser.add_argument('--without-bbox-decoder',
+                        action='store_true',
+                        help='Expore model without Bbox Decoder (for INT8 Quantization)')
     parser.add_argument('--work-dir',
                         default='./work_dirs',
                         help='Path to save export model')
@@ -129,7 +132,8 @@ def main():
     deploy_model = DeployModel(baseModel=baseModel,
                                backend=backend,
                                postprocess_cfg=postprocess_cfg,
-                               with_nms=not args.without_nms)
+                               with_nms=not args.without_nms,
+                               without_bbox_decoder=args.without_bbox_decoder)
     deploy_model.eval()
 
     fake_input = torch.randn(args.batch_size, 3,

deploy/easydeploy/model/model.py

@@ -2,6 +2,10 @@
 
 The reparameterization incorporates text embeddings as parameters into the model. For example, in the final classification layer, text embeddings are reparameterized into a simple 1x1 convolutional layer.
 
+<div align="center">
+<img width="600" src="../assets/reparameterize.png">
+</div>
+
 ### Key Advantages from Reparameterization
 
 > Reparameterized YOLO-World still has zero-shot ability!
@@ -15,13 +19,59 @@ For example, fine-tuning the **reparameterized YOLO-World** obtains *46.3 AP* on
 
 #### 1. Prepare cutstom text embeddings
 
-You need to generate the text embeddings 
-
+You need to generate the text embeddings by [`toos/generate_text_prompts.py`](../tools/generate_text_prompts.py) and save it as a `numpy.array` with shape `NxD`.
 
 #### 2. Reparameterizing
 
+Reparameterizing will generate a new checkpoint with text embeddings!
+
+Check those files first:
+
+* model checkpoint
+* text embeddings
 
+We mainly reparameterize two groups of modules:
+
+* head (`YOLOWorldHeadModule`)
+* neck (`MaxSigmoidCSPLayerWithTwoConv`)
+
+```bash
+python tools/reparameterize_yoloworld.py \
+    --model path/to/checkpoint \
+    --out-dir path/to/save/re-parameterized/ \
+    --text-embed path/to/text/embeddings \
+    --conv-neck
+```
 
 
 #### 3. Prepare the model config
 
+Please see the sample config: [`finetune_coco/yolo_world_v2_s_rep_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py`](../configs/finetune_coco/yolo_world_v2_s_rep_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py) for reparameterized training.
+
+
+* `RepConvMaxSigmoidCSPLayerWithTwoConv`:
+
+```python
+neck=dict(type='YOLOWorldPAFPN',
+        guide_channels=num_classes,
+        embed_channels=neck_embed_channels,
+        num_heads=neck_num_heads,
+        block_cfg=dict(type='RepConvMaxSigmoidCSPLayerWithTwoConv',
+                        guide_channels=num_classes)),
+```
+
+* `RepYOLOWorldHeadModule`:
+
+```python
+bbox_head=dict(head_module=dict(type='RepYOLOWorldHeadModule',
+                                embed_dims=text_channels,
+                                num_guide=num_classes,
+                                num_classes=num_classes)),
+
+```
+
+#### 4. Reparameterized Training
+
+**Reparameterized YOLO-World** is easier to fine-tune and can be treated as an enhanced and pre-trained YOLOv8!
+
+You can check [`finetune_coco/yolo_world_v2_s_rep_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py`](../configs/finetune_coco/yolo_world_v2_s_rep_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py) for more details.

deploy/export_onnx.py

@@ -49,6 +49,8 @@ def reparameterize_head(state_dict, embeds):
 
 def convert_neck_split_conv(input_state_dict, block_name, text_embeds,
                             num_heads):
+    if block_name + '.guide_fc.weight' not in input_state_dict:
+        return input_state_dict
     guide_fc_weight = input_state_dict[block_name + '.guide_fc.weight']
     guide_fc_bias = input_state_dict[block_name + '.guide_fc.bias']
     guide = text_embeds @ guide_fc_weight.transpose(0,
@@ -77,12 +79,15 @@ def convert_neck_weight(input_state_dict, block_name, embeds, num_heads):
 
 
 def reparameterize_neck(state_dict, embeds, type='conv'):
+
     neck_blocks = [
         'neck.top_down_layers.0.attn_block',
         'neck.top_down_layers.1.attn_block',
         'neck.bottom_up_layers.0.attn_block',
         'neck.bottom_up_layers.1.attn_block'
     ]
+    if "neck.top_down_layers.0.attn_block.bias" not in state_dict:
+        return state_dict
     for block in neck_blocks:
         num_heads = state_dict[block + '.bias'].shape[0]
         if type == 'conv':