def __init__(self, net_flag="vgg"):
super(LaneNet, self).__init__()
encode_num_blocks = 5
in_channels = [3, 64, 128, 256, 512]
out_channels = in_channels[1:] + [512]
self._net_flag = net_flag
if self._net_flag == 'vgg':
self._encoder = vgg_encoder.VGGEncoder(encode_num_blocks,
in_channels, out_channels)
decode_layers = ["pool5", "pool4", "pool3"]
decode_channels = out_channels[:-len(decode_layers) - 1:-1]
decode_last_stride = 8
self._decoder = fcn_decoder.FCNDecoder(decode_layers,
decode_channels,
decode_last_stride)
elif self._net_flag == 'sknet':
self._encoder = sknet_encoder.SKEncoder()
decode_channels = [1024, 512, 256]
decode_layers = ["pool3", "pool2", "pool1"]
decode_last_stride = 4
self._decoder = fcn_decoder.FCNDecoder(decode_layers,
decode_channels,
decode_last_stride)
self._score_layer = nn.Conv2d(64, 2, 1, bias=False)
self._pix_layer = nn.Sequential(nn.Conv2d(64, 3, 1, bias=False),
nn.ReLU())
def __init__(self, phase, net_flag='vgg'):
"""
"""
super(LaneNetBinarySeg, self).__init__()
self._net_flag = net_flag
self._phase = phase
if self._net_flag == 'vgg':
self._encoder = vgg_encoder.VGG16Encoder(phase=phase)
elif self._net_flag == 'dense':
self._encoder = dense_encoder.DenseEncoder(l=20, growthrate=8,
with_bc=True,
phase=self._phase,
n=5)
self._decoder = fcn_decoder.FCNDecoder()
return
def __init__(self, phase, net_flag='vgg'):
"""
:param phase:
:param net_flag:
"""
super(LaneNetBinarySeg, self).__init__()
self._phase = phase
self._net_flag = net_flag
if net_flag == 'vgg':
self._encoder = vgg_encoder.VGG16Encoder(phase=phase)
elif net_flag == 'dense':
self._encoder = dense_encoder.DenseEncoder(phase=phase,
L=20,
N=5,
growth_rate=8,
with_bc=True)
self._decoder = fcn_decoder.FCNDecoder()
def __init__(self, phase, net_flag='vgg'):
"""
"""
super(LaneNet, self).__init__()
self._net_flag = net_flag
self._phase = phase
if self._net_flag == 'vgg':
# 使用基于VGG16的特征编码类
self._encoder = vgg_encoder.VGG16Encoder(phase=phase)
elif self._net_flag == 'dense':
self._encoder = dense_encoder.DenseEncoder(l=20, growthrate=8,
with_bc=True,
phase=phase,
n=5)
# 确定使用一全卷积网络解码类
self._decoder = fcn_decoder.FCNDecoder(phase=phase)
return
def __init__(self, phase, net_flag='vgg'):
"""
Init for LaneNet class
"""
super(LaneNet, self).__init__()
self._net_flag = net_flag
self._phase = phase
if self._net_flag.lower() == 'vgg':
self._encoder = vgg_encoder.VGG16Encoder(phase=phase)
elif self._net_flag.lower() == 'dense':
self._encoder = dense_encoder.DenseEncoder(l=20, growthrate=8,
with_bc=True,
phase=phase,
n=5)
elif self._net_flag.lower() == 'enet':
# Need not encoder and decoder for enet.
return
self._decoder = fcn_decoder.FCNDecoder(phase=phase)
return
def __init__(self, phase, net_flag='vgg'):
"""
"""
super(LaneNet, self).__init__()
self._net_flag = net_flag
self._phase = phase
self._add_coord = coord_conv.AddCoords(CFG.TRAIN.IMG_HEIGHT,
CFG.TRAIN.IMG_WIDTH, False)
if self._net_flag == 'vgg':
self._encoder = vgg_encoder.VGG16Encoder(phase=phase)
elif self._net_flag == 'dense':
self._encoder = dense_encoder.DenseEncoder(l=20,
growthrate=8,
with_bc=True,
phase=phase,
n=5)
elif self._net_flag == 'mobile':
self._encoder = mobile_encoder.MobielnetV2Encoder(phase=phase)
self._decoder = fcn_decoder.FCNDecoder(phase=phase)
return
