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"""by lyuwenyu
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from src.core import register
__all__ = ['RTDETRPostProcessor']
@register
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)
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', )
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