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Running
on
Zero
Running
on
Zero
File size: 2,744 Bytes
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"""
Copied from RT-DETR (https://github.com/lyuwenyu/RT-DETR)
Copyright(c) 2023 lyuwenyu. All Rights Reserved.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
__all__ = ["DetDETRPostProcessor"]
from .box_revert import BoxProcessFormat, box_revert
def mod(a, b):
out = a - a // b * b
return out
class DetDETRPostProcessor(nn.Module):
def __init__(
self,
num_classes=80,
use_focal_loss=True,
num_top_queries=300,
box_process_format=BoxProcessFormat.RESIZE,
) -> None:
super().__init__()
self.use_focal_loss = use_focal_loss
self.num_top_queries = num_top_queries
self.num_classes = int(num_classes)
self.box_process_format = box_process_format
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, **kwargs):
logits, boxes = outputs["pred_logits"], outputs["pred_boxes"]
if self.use_focal_loss:
scores = F.sigmoid(logits)
scores, index = torch.topk(scores.flatten(1), self.num_top_queries, dim=-1)
labels = index % self.num_classes
# labels = mod(index, self.num_classes) # for tensorrt
index = index // self.num_classes
boxes = boxes.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes.shape[-1]))
else:
scores = F.softmax(logits)[:, :, :-1]
scores, labels = scores.max(dim=-1)
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])
)
if kwargs is not None:
boxes = box_revert(
boxes,
in_fmt="cxcywh",
out_fmt="xyxy",
process_fmt=self.box_process_format,
normalized=True,
**kwargs,
)
# TODO for onnx export
if self.deploy_mode:
return labels, boxes, scores
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
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