def __init__(self, num_classes, last_stride, model_path, neck, neck_feat,
model_name, pretrain_choice, cfg):
super(Baseline_reduce, self).__init__()
self.base = build_backbone(model_name, last_stride)
if 'regnet' in model_name:
self.in_planes = self.base.in_planes
if pretrain_choice == 'imagenet':
self.base.load_param(model_path)
print('Loading pretrained ImageNet model......')
if cfg.MODEL.POOLING_METHOD == 'GeM':
print('using GeM pooling')
self.gap = GeM()
else:
self.gap = nn.AdaptiveAvgPool2d(1)
self.num_classes = num_classes
self.feature_dim = cfg.MODEL.EMBEDDING_DIM
#self.reduce = nn.Linear(self.in_planes, self.feature_dim)
self.reduce = build_embedding_head(cfg.MODEL.EMBEDDING_HEAD,
self.in_planes, self.feature_dim,
cfg.MODEL.DROPOUT_PROB)
self.ID_LOSS_TYPE = cfg.MODEL.ID_LOSS_TYPE
self.bottleneck = nn.BatchNorm1d(self.feature_dim)
self.bottleneck.bias.requires_grad_(False) # no shift
if self.ID_LOSS_TYPE == 'arcface':
print('using {}'.format(self.ID_LOSS_TYPE))
self.classifier = Arcface(self.feature_dim,
self.num_classes,
s=cfg.SOLVER.COSINE_SCALE,
m=cfg.SOLVER.COSINE_MARGIN)
elif self.ID_LOSS_TYPE == 'cosface':
print('using {}'.format(self.ID_LOSS_TYPE))
self.classifier = Cosface(self.feature_dim,
self.num_classes,
s=cfg.SOLVER.COSINE_SCALE,
m=cfg.SOLVER.COSINE_MARGIN)
elif self.ID_LOSS_TYPE == 'amsoftmax':
print('using {}'.format(self.ID_LOSS_TYPE))
self.classifier = AMSoftmax(self.feature_dim,
self.num_classes,
s=cfg.SOLVER.COSINE_SCALE,
m=cfg.SOLVER.COSINE_MARGIN)
elif self.ID_LOSS_TYPE == 'circle':
print('using {}'.format(self.ID_LOSS_TYPE))
self.classifier = CircleLoss(self.feature_dim,
self.num_classes,
s=cfg.SOLVER.COSINE_SCALE,
m=cfg.SOLVER.COSINE_MARGIN)
else:
self.classifier = nn.Linear(self.feature_dim,
self.num_classes,
bias=False)
self.bottleneck.apply(weights_init_kaiming)
self.classifier.apply(weights_init_classifier)
def __init__(self, num_classes, last_stride, model_path, neck, neck_feat,
model_name, pretrain_choice, cfg):
super(Baseline_2_Head,
self).__init__(num_classes, last_stride, model_path, neck,
neck_feat, model_name, pretrain_choice, cfg)
self.gap_1 = GeM()
self.gap_2 = nn.AdaptiveAvgPool2d(1)
def __init__(self, num_classes, last_stride, model_path, neck, neck_feat,
model_name, pretrain_choice, cfg):
super(Baseline, self).__init__()
self.base = build_backbone(model_name, last_stride)
if 'regnet' in model_name:
self.in_planes = self.base.in_planes
if pretrain_choice == 'imagenet' and model_name not in [
'tf_efficientnet_l2_ns', 'seresnext50_32x4d', 'dm_nfnet_f0',
'nf_resnet50', 'dm_nfnet_f1'
]:
self.base.load_param(model_path)
print('Loading pretrained ImageNet model......')
if cfg.MODEL.POOLING_METHOD == 'GeM':
print('using GeM pooling')
self.gap = GeM()
else:
self.gap = nn.AdaptiveAvgPool2d(1)
self.num_classes = num_classes
self.neck = neck
self.neck_feat = neck_feat
self.ID_LOSS_TYPE = cfg.MODEL.ID_LOSS_TYPE
self.MultiHeads = MultiHeads(feature_dim=2048,
groups=32,
mode='S',
backbone_fc_dim=2048)
self.bottleneck = nn.BatchNorm1d(self.in_planes)
self.bottleneck.bias.requires_grad_(False) # no shift
#self.bottleneck = IBN(self.in_planes)
if self.ID_LOSS_TYPE == 'arcface':
print('using {}'.format(self.ID_LOSS_TYPE))
self.classifier = Arcface(self.in_planes,
self.num_classes,
s=cfg.SOLVER.COSINE_SCALE,
m=cfg.SOLVER.COSINE_MARGIN)
elif self.ID_LOSS_TYPE == 'cosface':
print('using {}'.format(self.ID_LOSS_TYPE))
self.classifier = Cosface(self.in_planes,
self.num_classes,
s=cfg.SOLVER.COSINE_SCALE,
m=cfg.SOLVER.COSINE_MARGIN)
elif self.ID_LOSS_TYPE == 'amsoftmax':
print('using {}'.format(self.ID_LOSS_TYPE))
self.classifier = AMSoftmax(self.in_planes,
self.num_classes,
s=cfg.SOLVER.COSINE_SCALE,
m=cfg.SOLVER.COSINE_MARGIN)
elif self.ID_LOSS_TYPE == 'circle':
print('using {}'.format(self.ID_LOSS_TYPE))
self.classifier = CircleLoss(self.in_planes,
self.num_classes,
s=cfg.SOLVER.COSINE_SCALE,
m=cfg.SOLVER.COSINE_MARGIN)
else:
self.classifier = nn.Linear(self.in_planes,
self.num_classes,
bias=False)
self.bottleneck.apply(weights_init_kaiming)
self.classifier.apply(weights_init_classifier)
self.reduce = nn.Sequential(
nn.Conv2d(3072, 2048, kernel_size=1, bias=False))
def __init__(self, num_classes, last_stride, model_path, neck, neck_feat,
model_name, pretrain_choice, cfg):
super(Baseline, self).__init__() #测试时model_name=resnet50_ibn_a
self.base = build_backbone(
model_name, last_stride) # 创建resnet50ibna模型（是否这一步不加载预训练参数？）
if 'regnet' in model_name:
self.in_planes = self.base.in_planes
if pretrain_choice == 'imagenet': # 测试时MODEL.PRETRAIN_CHOICE "('self')"不加载ImageNet的参数给resnet50ibna，后续在大main会加载reid训练好的参数
self.base.load_param(model_path)
print('Loading pretrained ImageNet model......')
if cfg.MODEL.POOLING_METHOD == 'GeM': # GeM pooling
print('using GeM pooling')
self.gap = GeM() # GeM pooling
else:
self.gap = nn.AdaptiveAvgPool2d(1) #普通avg pooling
self.num_classes = num_classes #重要参数，大main创建loader时得到
self.neck = neck #cfg.MODEL.NECK 是啥？
self.neck_feat = neck_feat #cfg.TEST.NECK_FEAT 是啥？
self.ID_LOSS_TYPE = cfg.MODEL.ID_LOSS_TYPE # 决定self.classifier类型 是啥？
self.bottleneck = nn.BatchNorm1d(
self.in_planes) # Batch Norm（dim=2048）
self.bottleneck.bias.requires_grad_(
False) # no shift 不训练Batch Norm的bias
#self.bottleneck = IBN(self.in_planes)
if self.ID_LOSS_TYPE == 'arcface':
print('using {}'.format(self.ID_LOSS_TYPE))
self.classifier = Arcface(self.in_planes,
self.num_classes,
s=cfg.SOLVER.COSINE_SCALE,
m=cfg.SOLVER.COSINE_MARGIN)
elif self.ID_LOSS_TYPE == 'cosface':
print('using {}'.format(self.ID_LOSS_TYPE))
self.classifier = Cosface(self.in_planes,
self.num_classes,
s=cfg.SOLVER.COSINE_SCALE,
m=cfg.SOLVER.COSINE_MARGIN)
elif self.ID_LOSS_TYPE == 'amsoftmax':
print('using {}'.format(self.ID_LOSS_TYPE))
self.classifier = AMSoftmax(self.in_planes,
self.num_classes,
s=cfg.SOLVER.COSINE_SCALE,
m=cfg.SOLVER.COSINE_MARGIN)
elif self.ID_LOSS_TYPE == 'circle':
print('using {}'.format(self.ID_LOSS_TYPE))
self.classifier = CircleLoss(self.in_planes,
self.num_classes,
s=cfg.SOLVER.COSINE_SCALE,
m=cfg.SOLVER.COSINE_MARGIN)
else:
self.classifier = nn.Linear(self.in_planes,
self.num_classes,
bias=False)
self.bottleneck.apply(weights_init_kaiming) #初始化BN层？
self.classifier.apply(weights_init_classifier) #初始化classifier？
#self.att = SpatialAttention2d(2048, 512)
'''大初始化结束'''