def initialize(self): self.bn0 = M.BatchNorm() self.c1 = M.ConvLayer(7, 64, stride=2, activation=M.PARAM_RELU, batch_norm=True, usebias=False) self.pool = M.MaxPool2D(3, 2) self.stage1 = Stage(64, num_units=3, stride=1) self.stage2 = Stage(128, num_units=4, stride=2) self.stage3 = Stage(256, num_units=6, stride=2) self.stage4 = Stage(512, num_units=3, stride=2) self.bn1 = M.BatchNorm() self.act = M.Activation(M.PARAM_RELU) self.ssh_c3_lateral = M.ConvLayer(1, 256, batch_norm=True, activation=M.PARAM_RELU) self.det3 = DETHead() self.head32 = RegressHead() self.ssh_c2_lateral = M.ConvLayer(1, 256, batch_norm=True, activation=M.PARAM_RELU) self.ssh_c3_upsampling = M.NNUpSample(2) self.ssh_c2_aggr = M.ConvLayer(3, 256, batch_norm=True, activation=M.PARAM_RELU) self.det2 = DETHead() self.head16 = RegressHead() self.ssh_m1_red_conv = M.ConvLayer(1, 256, batch_norm=True, activation=M.PARAM_RELU) self.ssh_c2_upsampling = M.NNUpSample(2) self.ssh_c1_aggr = M.ConvLayer(3, 256, batch_norm=True, activation=M.PARAM_RELU) self.det1 = DETHead() self.head8 = RegressHead()
def initialize(self, outchn, stride):
self.stride = stride
self.outchn = outchn
self.bn0 = M.BatchNorm()
self.c1 = M.ConvLayer(3,
outchn,
activation=M.PARAM_PRELU,
usebias=False,
batch_norm=True)
self.c2 = M.ConvLayer(3,
outchn,
stride=stride,
usebias=False,
batch_norm=True)
# se module
#self.c3 = M.ConvLayer(1, outchn//16, activation=M.PARAM_PRELU)
#self.c4 = M.ConvLayer(1, outchn, activation=M.PARAM_SIGMOID)
# shortcut
self.sc = M.ConvLayer(1,
outchn,
stride=stride,
usebias=False,
batch_norm=True)
def initialize(self, ksize, filters, stride, expand):
self.outchn = filters
self.expand = expand
self.stride = stride
outchn = filters * expand
self.bn0 = M.BatchNorm()
self.c0 = M.ConvLayer(1,
outchn,
usebias=False,
batch_norm=True,
activation=M.PARAM_PRELU)
self.c1 = M.DWConvLayer(ksize,
1,
stride=stride,
usebias=False,
batch_norm=True,
activation=M.PARAM_PRELU)
# se
self.se1 = M.ConvLayer(1, outchn // 8, activation=M.PARAM_PRELU)
self.se2 = M.ConvLayer(1, outchn, activation=M.PARAM_SIGMOID)
self.c2 = M.ConvLayer(1, filters, batch_norm=True, usebias=False)
self.sc = M.ConvLayer(1,
filters,
stride=stride,
batch_norm=True,
usebias=False)
def initialize(self, channel_list, blocknum_list, embedding_size, embedding_bn=True): self.c1 = M.ConvLayer(3, channel_list[0], activation=M.PARAM_PRELU) # self.u1 = ResBlock_v1(channel_list[1], stride=2) self.stage1 = Stage(channel_list[1], blocknum_list[0]) self.stage2 = Stage(channel_list[2], blocknum_list[1]) self.stage3 = Stage(channel_list[3], blocknum_list[2]) self.stage4 = Stage(channel_list[4], blocknum_list[3]) self.bn1 = M.BatchNorm() self.fc1 = M.Dense(512)
def initialize(self, chn, stride=1, shortcut=False): self.bn0 = M.BatchNorm() self.act = M.Activation(M.PARAM_RELU) self.c1 = M.ConvLayer(1, chn, activation=M.PARAM_RELU, batch_norm=True, usebias=False) self.c2 = M.ConvLayer(3, chn, stride=stride, activation=M.PARAM_RELU, batch_norm=True, usebias=False) self.c3 = M.ConvLayer(1, chn*4, usebias=False) self.shortcut = shortcut if shortcut: self.sc = M.ConvLayer(1, chn*4, stride=stride, usebias=False)
def initialize(self, channel_list, blocknum_list, embedding_size, embedding_bn=True):
self.c1 = M.ConvLayer(3, channel_list[0], 1, usebias=False, activation=M.PARAM_PRELU, batch_norm=True)
# self.u1 = ResBlock_v1(channel_list[1], stride=2)
self.stage1 = Stage(channel_list[1], blocknum_list[0])
self.stage2 = Stage(channel_list[2], blocknum_list[1])
self.stage3 = Stage(channel_list[3], blocknum_list[2])
self.stage4 = Stage(channel_list[4], blocknum_list[3])
self.bn1 = M.BatchNorm()
print('Embedding_size:', embedding_size)
self.fc1 = M.Dense(embedding_size, usebias=False)
def initialize(self, outchn=512, dilation=1, k=3):
self.bn = M.BatchNorm()
self.c1 = M.ConvLayer1D(k,
outchn,
dilation_rate=dilation,
activation=M.PARAM_PRELU,
batch_norm=True,
usebias=False,
pad='VALID')
self.c2 = M.ConvLayer1D(3, outchn, pad='VALID')
def initialize(self, channel_list, blocknum_list, drop_prob):
self.c1 = M.ConvLayer(3,
channel_list[0],
usebias=False,
batch_norm=True,
activation=M.PARAM_PRELU)
self.stage1 = Stage(channel_list[1], blocknum_list[0], drop_prob)
self.stage2 = Stage(channel_list[2], blocknum_list[1], drop_prob)
self.stage3 = Stage(channel_list[3], blocknum_list[2], drop_prob)
self.stage4 = Stage(channel_list[4], blocknum_list[3], drop_prob)
self.bn1 = M.BatchNorm()
self.fc1 = M.Dense(512, usebias=False, batch_norm=True)
def initialize(self, channel_list, blocknum_list, embedding_size):
self.c1 = M.ConvLayer(3,
channel_list[0],
1,
usebias=False,
activation=M.PARAM_PRELU,
batch_norm=True)
# self.u1 = ResBlock_v1(channel_list[1], stride=2)
self.stage1 = Stage(channel_list[1], blocknum_list[0])
self.stage2 = Stage(channel_list[2], blocknum_list[1])
self.stage3 = Stage(channel_list[3], blocknum_list[2])
self.stage4 = Stage(channel_list[4], blocknum_list[3])
self.bn1 = M.BatchNorm()
self.fcs = nn.ModuleList()
if isinstance(embedding_size, list):
for size in embedding_size:
self.fcs.append(M.Dense(size, usebias=False))
else:
self.fcs.append(M.Dense(embedding_size, usebias=False))
def initialize(self, out):
self.c1 = M.ConvLayer(1, out // 2, stride=2, usebias=False)
self.c2 = M.ConvLayer(1, out // 2, stride=2, usebias=False)
self.bn = M.BatchNorm()
def initialize(self, stride, bn=False): self.bn = bn self.p = M.AvgPool2D(3, stride) if self.bn: self.batch_norm = M.BatchNorm()
