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Running
on
Zero
Running
on
Zero
File size: 2,962 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
from ..core import register
__all__ = ['PostProcessor']
def mod(a, b):
out = a - a // b * b
return out
@register()
class PostProcessor(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 = int(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: torch.Tensor):
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, dim=-1)
# labels = index % self.num_classes
labels = mod(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]))
if self.deploy_mode:
return labels, boxes, scores
if self.remap_mscoco_category:
from ..data.dataset 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
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