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) self._feat_stride = [8, 8, 4] self._feat_compress = [1. / float(self._feat_stride[0]), ] self._num_layers = num_layers self._scope = 'resnet_3d_%d' % num_layers self._decide_blocks()
def __init__(self): Network.__init__(self) self._feat_stride = [16, ] self._feat_compress = [1. / float(self._feat_stride[0]), ] self._net_conv_channels = 514 #self._net_conv_channels = 512 self._fc7_channels = 4096
def __init__(self, num_layers=50):
Network.__init__(self)
self._feat_stride = cfg.ANCHOR_STRIDES
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) 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._net_conv_channels = 1024 self._fc7_channels = 2048
def __init__(self, num_layers=50):
Network.__init__(self)
if (cfg.USE_FPN):
#WAS 4 now is 8 due to downsample layer after FPN
if (cfg.POOLING_MODE == 'multiscale'):
self._feat_stride = 4
#DEPRECATED (see lidar for explanation)
#else:
# self._feat_stride = 8
self._fpn_en = True
self._batchnorm_en = True
self._net_conv_channels = 256
self._roi_pooling_channels = cfg.POOLING_SIZE * cfg.POOLING_SIZE * self._net_conv_channels
else:
self._feat_stride = 16
self._fpn_en = False
self._batchnorm_en = True
self._net_conv_channels = 1024
self._roi_pooling_channels = 1024
self._fc7_channels = 2048
self.inplanes = 64
self._num_resnet_layers = num_layers
if (cfg.UC.EN_BBOX_EPISTEMIC or cfg.UC.EN_CLS_EPISTEMIC):
self._det_net_channels = int(self._fc7_channels / 4)
self._dropout_en = True
self._cls_drop_rate = 0.3
self._bbox_drop_rate = 0.1
self._resnet_drop_rate = 0.5
else:
self._det_net_channels = self._fc7_channels
self._dropout_en = False
self._cls_drop_rate = 0.0
self._bbox_drop_rate = 0.0
self._resnet_drop_rate = 0.0
def _crop_pool_layer(self, bottom, rois):
if cfg.FPN:
return Network._crop_pool_layer_fpn(self, bottom, rois,
cfg.RESNET.MAX_POOL)
else:
return Network._crop_pool_layer(self, bottom, rois,
cfg.RESNET.MAX_POOL)
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): 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, num_layers=50):
Network.__init__(self)
self._feat_stride = [
16,
]
self._num_layers = num_layers
self._scope = 'resnet_v1_%d' % num_layers
self._decide_blocks()
def __init__(self):
Network.__init__(self)
if cfg.REMOVE_POOLING:
self._feat_stride = [8, ]
else:
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._scope = 'vgg_16'
def __init__(self):
Network.__init__(self)
self._feat_stride = [
16,
]
self._feat_compress = [
1. / float(self._feat_stride[0]),
]
self._net_conv_channels = cfg.FC6_IN_CHANNEL
self._fc7_channels = cfg.FC7_OUT_CHANNEL
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, batch_size=1, num_layers=50):
Network.__init__(self, batch_size=batch_size)
self._predictions = {i: {} for i in range(5, 1, -1)}
self._anchor_targets = {i: {} for i in range(5, 1, -1)}
self._proposal_targets = {i: {} for i in range(5, 1, -1)}
self._num_layers = num_layers
self._arch = 'res_v1_%d' % num_layers
self._resnet_scope = 'resnet_v1_%d' % num_layers
self._feat_stride = {}
def __init__(self):
Network.__init__(self)
self._feat_stride = [
16,
]
self._feat_compress = [
1. / float(self._feat_stride[0]),
]
self._m = ConvNet(seed=0)
self._scope = 'alexnet_tf'
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
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.resnet_constructor = ImagenetModel(num_layers)
self._scope = 'resnet_model'
def __init__(self):
Network.__init__(self)
# [Hand Detection --> change to 4]
self._feat_stride = [
4,
]
self._feat_compress = [
1. / float(self._feat_stride[0]),
]
self._scope = 'vgg_16'
def __init__(self, num_layers=41):
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_50'
self._decide_blocks()
def __init__(self):
Network.__init__(self)
# config which branch contained in the SSH -- should be the format of ['M1', 'M2', 'M3']
self._feat_branches = ['M1', 'M2', 'M3']
self._Module_boxes = {'M1': 128, 'M2': 256, 'M3': 256}
self._feat_stride = {"M1": 8, 'M2': 16, 'M3': 32}
self._feat_layers = {
"M1": ['conv4_3', 'conv5_3'],
'M2': 'conv5_3',
'M3': 'conv5_3'
}
self._scope = 'vgg_16'
self.end_points = {}
def __init__(self, nof_ent_classes, nof_rel_classes, 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()
self.nof_ent_classes = nof_ent_classes
self.nof_rel_classes = nof_rel_classes
def __init__(self, darknet53_npz_path=None):
Network.__init__(self)
self._feat_branches = {'M1', 'M2', 'M3'}
self._feat_stride = {'M1': 8, 'M2': 16, 'M3': 32}
self._feat_layers = {
'M1': ['res10', 'res18'],
'M2': 'res18',
'M3': 'res22'
}
self._Module_boxes = {'M1': 128, 'M2': 256, 'M3': 256}
self.end_points = {}
self._scope = 'Darknet53'
self.darknet53_npz_path = darknet53_npz_path
def __init__(self):
Network.__init__(self)
# config which branch contained in the SSH should be the format of ['M1', 'M2', 'M3']
self._feat_branches = ['M1', 'M2', 'M3']
self._feat_stride = {'M1': 8, 'M2': 16, 'M3': 32}
self._Module_boxes = {'M1': 128, 'M2': 256, 'M3': 256}
self._feat_layers = {
'M1': ['Conv2d_5_pointwise', 'Conv2d_13_pointwise'],
'M2': 'Conv2d_13_pointwise',
'M3': 'Conv2d_13_pointwise'
}
self.end_points = {}
self._depth_multiplier = cfg.MOBILENET.DEPTH_MULTIPLIER
self._scope = 'MobilenetV1'
def __init__(self, key="gpu"): Network.__init__(self) self.mobilenet, self.urls = nasnet(pretrained=False, key=key) self._feat_stride = [16, ] self._feat_compress = [1. / float(self._feat_stride[0]), ] self._depth_multiplier = cfg.MOBILENET.DEPTH_MULTIPLIER self._net_conv_channels = 320 self._fc7_channels = 1280 self._net_conv_channels = self.mobilenet.feature_mix_layer.in_channels self._fc7_channels = self.mobilenet.feature_mix_layer.in_channels * 4 # out_channels? if key == "cpu": self._net_conv_channels = self.mobilenet.feature_mix_layer.in_channels self._fc7_channels = self.mobilenet.feature_mix_layer.out_channels
def __init__(self, num_layers=50):
Network.__init__(self)
if cfg.FPN:
self._feat_stride = [4, 8, 16, 32, 64]
self._net_conv_channels = 256
self._fc7_channels = 1024
else:
self._feat_stride = [
16,
]
self._net_conv_channels = 1024
self._fc7_channels = 2048
self._feat_compress = [
1. / float(self._feat_stride[0]),
]
self._num_layers = num_layers
def __init__(self, num_layers=50):
Network.__init__(self)
# config which branch contained in the SSH should be the format of ['M1', 'M2', 'M3']
self._feat_branches = ['M1', 'M2', 'M3']
self._feat_stride = {'M1': 8, 'M2': 16, 'M3': 32}
self._Module_boxes = {'M1': 128, 'M2': 256, 'M3': 256}
# self._feat_layers = {'M1': ['block2', 'block3'], 'M2': 'block3', 'M3': 'block3'}
self._feat_layers = {
'M1': ['block2', 'block4'],
'M2': 'block4',
'M3': 'block4'
}
self.end_points = {}
self._num_layers = num_layers
self._scope = 'resnet_v1_%d' % num_layers
self._decide_blocks()
def __init__(self):
Network.__init__(self)
# config which branch contained in the SSH should be the format of ['M1', 'M2', 'M3']
self._feat_branches = ['M1', 'M2', 'M3']
self._feat_stride = {'M1': 8, 'M2': 16, 'M3': 32}
self._feat_layers = {
'M1': ['layer_5', 'layer_14'],
'M2': 'layer_14',
'M3': 'layer_19'
}
# self._feat_layers = {'M1': ['layer_5/expansion_output', 'layer_19'], 'M2': 'layer_19',
# 'M3': 'layer_19'}
self._Module_boxes = {'M1': 128, 'M2': 256, 'M3': 256}
self.end_points = {}
self._depth_multiplier = cfg.MOBILENET_V2.DEPTH_MULTIPLIER
self._min_depth = cfg.MOBILENET_V2.MIN_DEPTH
self._scope = 'MobilenetV2'
def __init__(self, opt, batch_size=1, num_layers=50):
Network.__init__(self, batch_size=batch_size)
self._num_layers = num_layers
# language rnn encoder
self.rnn_encoder = RNNEncoder(
vocab_size=opt['vocab_size'],
word_embedding_size=opt['word_embedding_size'],
word_vec_size=opt['word_vec_size'],
hidden_size=opt['rnn_hidden_size'],
bidirectional=opt['bidirectional'] > 0,
input_dropout_p=opt['word_drop_out'],
dropout_p=opt['rnn_drop_out'],
n_layers=opt['rnn_num_layers'],
rnn_type=opt['rnn_type'],
variable_lengths=opt['variable_lengths'] > 0)
self._rnn_num_layers = opt['rnn_num_layers']
self._rnn_hidden_size = opt['rnn_hidden_size']
self._rnn_num_dirs = 2 if opt['bidirectional'] > 0 else 1
self._C4_feat_dim = opt['C4_feat_dim']
def __init__(self, num_layers=50):
Network.__init__(self)
if(cfg.USE_FPN):
if(cfg.POOLING_MODE == 'multiscale'):
self._feat_stride = 4
#DEPRECATED, only do multiscale pooling now
#else:
# self._feat_stride = 8
self._fpn_en = True
self._batchnorm_en = True
self._net_conv_channels = 256
self._roi_pooling_channels = cfg.POOLING_SIZE*cfg.POOLING_SIZE*self._net_conv_channels
elif(cfg.USE_LIDAR_FPN):
self._feat_stride = 8
self._fpn_en = True
self._batchnorm_en = False
self._net_conv_channels = 1024
self._roi_pooling_channels = cfg.POOLING_SIZE*cfg.POOLING_SIZE*self._net_conv_channels
else:
self._feat_stride = 16
self._fpn_en = False
self._net_conv_channels = 1024
self._roi_pooling_channels = 1024
self._batchnorm_en = False
self._fc7_channels = 2048
self.inplanes = 64
self._num_resnet_layers = num_layers
if(cfg.UC.EN_BBOX_EPISTEMIC or cfg.UC.EN_CLS_EPISTEMIC):
self._det_net_channels = int(self._fc7_channels/4)
self._dropout_en = True
self._resnet_drop_rate = 0.5
self._cls_drop_rate = 0.2
self._bbox_drop_rate = 0.5
else:
self._det_net_channels = self._fc7_channels
self._dropout_en = False
self._resnet_drop_rate = 0.0
self._cls_drop_rate = 0.0
self._bbox_drop_rate = 0.0
self.num_lidar_channels = cfg.LIDAR.NUM_CHANNEL
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):
Network.__init__(self)
self._feat_stride = [16, ]
self._scope = 'mobilenet_v2'
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, batch_size=1): Network.__init__(self, batch_size=batch_size) self._depth_multiplier = cfg.MOBILENET.DEPTH_MULTIPLIER self._scope = 'MobilenetV1'
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, num_layers=50):
Network.__init__(self, batch_size=batch_size)
self._num_layers = num_layers
self._resnet_scope = 'resnet_v1_%d' % num_layers
self.bottleneck_func = resnet_v1.bottleneck
self._end_points_collection = self._resnet_scope + '_end_points'
def __init__(self):
Network.__init__(self)
self._feat_stride = [16, ]
self._scope = 'vgg_16'
def _crop_pool_layer(self, bottom, rois): return Network._crop_pool_layer(self, bottom, rois, cfg.RESNET.MAX_POOL)
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, with_dropout=False): Network.__init__(self) self._feat_stride = [16, ] self._feat_compress = [1. / float(self._feat_stride[0]), ] self._scope = 'alexnet_v2' self.with_dropout = with_dropout
def __init__(self, batch_size=1): Network.__init__(self, batch_size=batch_size) self._arch = 'res101'
