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"""
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Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR)
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Copyright(c) 2023 lyuwenyu. All Rights Reserved.
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision
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__all__ = ["DetDETRPostProcessor"]
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from .box_revert import BoxProcessFormat, box_revert
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def mod(a, b):
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out = a - a // b * b
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return out
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class DetDETRPostProcessor(nn.Module):
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def __init__(
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self,
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num_classes=80,
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use_focal_loss=True,
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num_top_queries=300,
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box_process_format=BoxProcessFormat.RESIZE,
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) -> None:
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super().__init__()
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self.use_focal_loss = use_focal_loss
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self.num_top_queries = num_top_queries
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self.num_classes = int(num_classes)
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self.box_process_format = box_process_format
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self.deploy_mode = False
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def extra_repr(self) -> str:
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return f"use_focal_loss={self.use_focal_loss}, num_classes={self.num_classes}, num_top_queries={self.num_top_queries}"
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def forward(self, outputs, **kwargs):
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logits, boxes = outputs["pred_logits"], outputs["pred_boxes"]
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if self.use_focal_loss:
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scores = F.sigmoid(logits)
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scores, index = torch.topk(scores.flatten(1), self.num_top_queries, dim=-1)
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labels = index % self.num_classes
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index = index // self.num_classes
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boxes = boxes.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes.shape[-1]))
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else:
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scores = F.softmax(logits)[:, :, :-1]
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scores, labels = scores.max(dim=-1)
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if scores.shape[1] > self.num_top_queries:
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scores, index = torch.topk(scores, self.num_top_queries, dim=-1)
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labels = torch.gather(labels, dim=1, index=index)
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boxes = torch.gather(
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boxes, dim=1, index=index.unsqueeze(-1).tile(1, 1, boxes.shape[-1])
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)
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if kwargs is not None:
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boxes = box_revert(
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boxes,
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in_fmt="cxcywh",
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out_fmt="xyxy",
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process_fmt=self.box_process_format,
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normalized=True,
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**kwargs,
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)
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if self.deploy_mode:
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return labels, boxes, scores
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results = []
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for lab, box, sco in zip(labels, boxes, scores):
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result = dict(labels=lab, boxes=box, scores=sco)
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results.append(result)
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return results
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def deploy(
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self,
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):
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self.eval()
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self.deploy_mode = True
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return self
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