Create postprocessor

postprocessor

@@ -0,0 +1,79 @@
+"""by lyuwenyu
+"""
+
+import json
+import torch 
+import torch.nn as nn 
+import torch.nn.functional as F 
+import torchvision
+
+
+__all__ = ['RTDETRPostProcessor']
+
+
+class RTDETRPostProcessor(nn.Module):
+    __share__ = ['num_classes', 'use_focal_loss', 'num_top_queries', 'remap_mscoco_category']
+    
+    def __init__(self, num_classes=80, use_focal_loss=True, num_top_queries=300, remap_mscoco_category=False) -> None:
+        super().__init__()
+        self.use_focal_loss = use_focal_loss
+        self.num_top_queries = num_top_queries
+        self.num_classes = num_classes
+        self.remap_mscoco_category = remap_mscoco_category 
+        self.deploy_mode = False 
+
+
+    def extra_repr(self) -> str:
+        return f'use_focal_loss={self.use_focal_loss}, num_classes={self.num_classes}, num_top_queries={self.num_top_queries}'
+    
+    # def forward(self, outputs, orig_target_sizes):
+    def forward(self, outputs, orig_target_sizes):
+
+        logits, boxes = outputs['pred_logits'], outputs['pred_boxes']
+        # orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)        
+
+        bbox_pred = torchvision.ops.box_convert(boxes, in_fmt='cxcywh', out_fmt='xyxy')
+        bbox_pred *= orig_target_sizes.repeat(1, 2).unsqueeze(1)
+
+        if self.use_focal_loss:
+            scores = F.sigmoid(logits)
+            scores, index = torch.topk(scores.flatten(1), self.num_top_queries, axis=-1)
+            labels = index % self.num_classes
+            index = index // self.num_classes
+            boxes = bbox_pred.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, bbox_pred.shape[-1]))
+            
+        else:
+            scores = F.softmax(logits)[:, :, :-1]
+            scores, labels = scores.max(dim=-1)
+            boxes = bbox_pred
+            if scores.shape[1] > self.num_top_queries:
+                scores, index = torch.topk(scores, self.num_top_queries, dim=-1)
+                labels = torch.gather(labels, dim=1, index=index)
+                boxes = torch.gather(boxes, dim=1, index=index.unsqueeze(-1).tile(1, 1, boxes.shape[-1]))
+
+        # TODO for onnx export
+        if self.deploy_mode:
+            return labels, boxes, scores
+
+        # TODO
+        if self.remap_mscoco_category:
+            from ...data.coco import mscoco_label2category
+            labels = torch.tensor([mscoco_label2category[int(x.item())] for x in labels.flatten()])\
+                .to(boxes.device).reshape(labels.shape)
+
+        results = []
+        for lab, box, sco in zip(labels, boxes, scores):
+            result = dict(labels=lab, boxes=box, scores=sco)
+            results.append(result)
+        
+        return results
+        
+
+    def deploy(self, ):
+        self.eval()
+        self.deploy_mode = True
+        return self 
+
+    @property
+    def iou_types(self, ):
+        return ('bbox', )