def __init__(self): Network.__init__(self) self._feat_stride = [16, ] self._feat_compress = [1. / float(self._feat_stride[0]), ] self._net_conv_channels = 512 self._fc7_channels = 4096 self._init_head_tail()
def __init__(self): Network.__init__(self) self._feat_stride = [16, ] self._feat_compress = [1. / float(self._feat_stride[0]), ] self._depth_multiplier = cfg.MOBILENET.DEPTH_MULTIPLIER self._net_conv_channels = 512 self._fc7_channels = 1024
def __init__(self, feat_strdie=(16, ), anchor_scales=(8, 16, 32), anchor_ratios=(0.5, 1, 2)): Network.__init__(self) self._channels['head'] = 512 self._channels['tail'] = 4096 self._feat_stride = feat_strdie self._anchor_scales = anchor_scales self._anchor_ratios = anchor_ratios self._num_anchors = len(anchor_scales)*len(anchor_ratios)
def __init__(self):
Network.__init__(self)
self._feat_stride = [
16,
]
self._feat_compress = [
1. / float(self._feat_stride[0]),
]
self._scope = 'vgg_16'
def __init__(self):
Network.__init__(self)
self._feat_stride = [
16,
]
self._feat_compress = [
1. / float(self._feat_stride[0]),
]
self._depth_multiplier = cfg.MOBILENET.DEPTH_MULTIPLIER
self._scope = 'MobilenetV1'
def __init__(self, num_layers=50):
Network.__init__(self)
self._feat_stride = [
16,
]
self._feat_compress = [
1. / float(self._feat_stride[0]),
]
self._num_layers = num_layers
self._net_conv_channels = 1024
self._fc7_channels = 2048
def __init__(self, num_layers=50):
Network.__init__(self)
self._feat_stride = [
16,
]
self._feat_compress = [
1. / float(self._feat_stride[0]),
]
self._num_layers = num_layers
self._scope = 'resnet_v1_%d' % num_layers
self._decide_blocks()
def __init__(self, num_layers=50):
Network.__init__(self)
# conv1(feet_stride:2) * pool1(feet_stride:2) * block1(feet_stride:2) *
# block2(feet_stride:2) * block3(feet_stride:1) = 16
self._feat_stride = [
16,
]
self._feat_compress = [
1. / float(self._feat_stride[0]),
]
self._num_layers = num_layers
self._scope = 'resnet_v1_%d' % num_layers
self._decide_blocks()
def __init__(self,
resnet_type,
feat_strdie=(4, 8, 16, 32, 64),
anchor_scales=(32, 64, 128, 256, 512),
anchor_ratios=(0.5, 1, 2)):
Network.__init__(self)
self._resnet_type = resnet_type
self._channels['head'] = None
self._channels['tail'] = None
self._lateral_channel = 256
self._feat_stride = feat_strdie
self._anchor_scales = anchor_scales
self._anchor_ratios = anchor_ratios
self._num_anchors = len(anchor_ratios)
self._residual_block = None
def __init__(self,
batch_size=1,
num_layers=121,
reduction=0.5,
dropout_rate=None):
Network.__init__(self, batch_size=batch_size)
self._num_layers = num_layers
self._dense_scope = 'densenet%d' % num_layers
# if self._num_layers == 121:
# self.num_filters = 64
# self.growth_rate = 32
# elif self._num_layers == 161:
# self.num_filters = 96
# self.growth_rate = 48
self.reduction = reduction
self.dropout_rate = dropout_rate
def __init__(self, batch_size=1, data_format='NHWC',
width=1.0, lr=0.001, weight_decay = 0.0005,
label_smoothing=0.0):
Network.__init__(self, batch_size=batch_size)
self.dropout_keep_prob = 0.8
self.is_training = True
self.min_depth = 8
self.depth = 1.0
self.conv_defs = None
self.spatial_squeeze = True
self.reuse = None
self._scope = 'MobileNetV2'
self.global_pool = False
self.dw_code = None
self.ratio_code = None
self.se = 1
self.data_format = data_format
self.lr = lr
self.depth_multiplier = width
self.weight_decay = weight_decay
self.label_smoothing = label_smoothing
def __init__(self, batch_size=1):
Network.__init__(self, batch_size=batch_size)
def __init__(self, batch_size=1, num_layers=50):
Network.__init__(self, batch_size=batch_size)
self._num_layers = num_layers
self._resnet_scope = 'resnet_v1_%d' % num_layers
def __init__(self, batch_size=1):
Network.__init__(self, batch_size=batch_size)
LSTM.__init__(self, self.k, self.lstm_out, self.batch_size)
def __init__(self, batch_size=1, num_layers=50):
Network.__init__(self, batch_size=batch_size)
self._num_layers = num_layers
self._resnet_scope = 'resnet_v1_%d' % num_layers
print('build_network init, _num_layers', self._num_layers)
def __init__(self, batch_size=1):
Network.__init__(self,batch_size)
# 原图到特征图缩放比例，以及压缩参数
self._feat_stride = [16, ]
self._feat_compress = [1. / float(self._feat_stride[0]), ]
self._scope = 'vgg_16'