# encoding: utf-8
|
"""
|
@author: liaoxingyu
|
@contact: sherlockliao01@gmail.com
|
"""
|
|
from layers import *
|
from modeling.losses import *
|
from .build import REID_HEADS_REGISTRY
|
from utils.weight_init import weights_init_classifier
|
|
|
@REID_HEADS_REGISTRY.register()
|
class LinearHead(nn.Module):
|
def __init__(self, cfg, in_feat, num_classes, pool_layer):
|
super().__init__()
|
self.pool_layer = pool_layer
|
|
# identity classification layer
|
cls_type = cfg.MODEL.HEADS.CLS_LAYER
|
if cls_type == 'linear': self.classifier = nn.Linear(in_feat, num_classes, bias=False)
|
elif cls_type == 'arcface': self.classifier = Arcface(cfg, in_feat, num_classes)
|
elif cls_type == 'circle': self.classifier = Circle(cfg, in_feat, num_classes)
|
else:
|
raise KeyError(f"{cls_type} is invalid, please choose from "
|
f"'linear', 'arcface' and 'circle'.")
|
|
self.classifier.apply(weights_init_classifier)
|
|
def forward(self, features, targets=None):
|
"""
|
See :class:`ReIDHeads.forward`.
|
"""
|
global_feat = self.pool_layer(features)
|
global_feat = global_feat[..., 0, 0]
|
|
# Evaluation
|
if not self.training: return global_feat
|
|
# Training
|
try:
|
cls_outputs = self.classifier(global_feat)
|
pred_class_logits = cls_outputs.detach()
|
except TypeError:
|
cls_outputs = self.classifier(global_feat, targets)
|
pred_class_logits = F.linear(F.normalize(global_feat.detach()), F.normalize(self.classifier.weight.detach()))
|
# Log prediction accuracy
|
CrossEntropyLoss.log_accuracy(pred_class_logits, targets)
|
|
return cls_outputs, global_feat
|
