def __init__(self, num_classes, trunk='hrnetv2', criterion=None):
super(Basic, self).__init__()
self.criterion = criterion
self.backbone, _, _, high_level_ch = get_trunk(trunk_name=trunk,
output_stride=8)
self.seg_head = make_seg_head(in_ch=high_level_ch, out_ch=num_classes)
initialize_weights(self.seg_head)
def __init__(self,
num_classes,
trunk='resnet-50',
criterion=None,
use_dpc=False,
init_all=False,
output_stride=8):
super(DeepV3ATTN, self).__init__()
self.criterion = criterion
self.backbone, _s2_ch, _s4_ch, high_level_ch = \
get_trunk(trunk, output_stride=output_stride)
#self.aspp, aspp_out_ch = get_aspp(high_level_ch,
# bottleneck_ch=256,
# output_stride=output_stride,
# dpc=use_dpc)
#self.attn = APNB(in_channels=high_level_ch, out_channels=high_level_ch, key_channels=256, value_channels=256, dropout=0.5, sizes=([1]), norm_type='batchnorm', psp_size=(1,3,6,8))
self.attn = AFNB(low_in_channels=2048,
high_in_channels=4096,
out_channels=2048,
key_channels=1024,
value_channels=2048,
dropout=0.5,
sizes=([1]),
norm_type='batchnorm',
psp_size=(1, 3, 6, 8))
self.final = make_seg_head(in_ch=high_level_ch, out_ch=num_classes)
initialize_weights(self.attn)
initialize_weights(self.final)
def __init__(self, num_classes, trunk='hrnetv2', criterion=None):
super(MscaleBasic, self).__init__()
self.criterion = criterion
self.backbone, _, _, high_level_ch = get_trunk(trunk_name=trunk,
output_stride=8)
self.cls_head = make_seg_head(in_ch=high_level_ch, out_ch=num_classes)
self.scale_attn = make_attn_head(in_ch=high_level_ch, out_ch=1)
def __init__(self, num_classes, trunk='hrnetv2', criterion=None):
super(ASPP, self).__init__()
self.criterion = criterion
self.backbone, _, _, high_level_ch = get_trunk(trunk)
self.aspp, aspp_out_ch = get_aspp(high_level_ch,
bottleneck_ch=cfg.MODEL.ASPP_BOT_CH,
output_stride=8)
self.bot_aspp = nn.Conv2d(aspp_out_ch, 256, kernel_size=1, bias=False)
self.final = make_seg_head(in_ch=256, out_ch=num_classes)
initialize_weights(self.final, self.bot_aspp, self.aspp)
def __init__(self, num_classes, trunk='resnet-50', criterion=None,
use_dpc=False, init_all=False, output_stride=8):
super(DeepV3, self).__init__()
self.criterion = criterion
self.backbone, _s2_ch, _s4_ch, high_level_ch = \
get_trunk(trunk, output_stride=output_stride)
self.aspp, aspp_out_ch = get_aspp(high_level_ch,
bottleneck_ch=256,
output_stride=output_stride,
dpc=use_dpc)
self.final = make_seg_head(in_ch=aspp_out_ch, out_ch=num_classes)
initialize_weights(self.aspp)
initialize_weights(self.final)