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): 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._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) 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 __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, 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) # 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, 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): 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, 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, 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, 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
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): Network.__init__(self, batch_size=batch_size) self._arch = 'res101'
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): Network.__init__(self) self._feat_stride = [16, ] self._scope = 'vgg_16'