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):
self.c1 = M.ConvLayer(3,
8,
stride=2,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c2 = M.DWConvLayer(3,
1,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c3 = M.ConvLayer(1,
16,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c4 = M.DWConvLayer(3,
1,
stride=2,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c5 = M.ConvLayer(1,
32,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c6 = M.DWConvLayer(3,
1,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c7 = M.ConvLayer(1,
32,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c8 = M.DWConvLayer(3,
1,
stride=2,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c9 = M.ConvLayer(1,
64,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c10 = M.DWConvLayer(3,
1,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c11 = M.ConvLayer(1,
64,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c12 = M.DWConvLayer(3,
1,
stride=2,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c13 = M.ConvLayer(1,
128,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c14 = M.DWConvLayer(3,
1,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c15 = M.ConvLayer(1,
128,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c16 = M.DWConvLayer(3,
1,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c17 = M.ConvLayer(1,
128,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c18 = M.DWConvLayer(3,
1,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c19 = M.ConvLayer(1,
128,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c20 = M.DWConvLayer(3,
1,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c21 = M.ConvLayer(1,
128,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c22 = M.DWConvLayer(3,
1,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c23 = M.ConvLayer(1,
128,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c24 = M.DWConvLayer(3,
1,
stride=2,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c25 = M.ConvLayer(1,
256,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c26 = M.DWConvLayer(3,
1,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.c27 = M.ConvLayer(1,
256,
usebias=False,
batch_norm=True,
activation=M.PARAM_RELU)
self.bn_eps(1e-5)
self.rf_c3_lateral = M.ConvLayer(1,
64,
batch_norm=True,
activation=M.PARAM_RELU)
self.rf_c3_lateral.bn_eps(2e-5)
self.det3 = DETHead()
self.det3.bn_eps(2e-5)
self.rf_c2_lateral = M.ConvLayer(1,
64,
batch_norm=True,
activation=M.PARAM_RELU)
self.rf_c2_lateral.bn_eps(2e-5)
self.rf_c3_upsampling = M.NNUpSample(2)
self.rf_c2_aggr = M.ConvLayer(3,
64,
batch_norm=True,
activation=M.PARAM_RELU)
self.rf_c2_aggr.bn_eps(2e-5)
self.det2 = DETHead()
self.det2.bn_eps(2e-5)
self.rf_c1_red_conv = M.ConvLayer(1,
64,
batch_norm=True,
activation=M.PARAM_RELU)
self.rf_c1_red_conv.bn_eps(2e-5)
self.rf_c2_upsampling = M.NNUpSample(2)
self.rf_c1_aggr = M.ConvLayer(3,
64,
batch_norm=True,
activation=M.PARAM_RELU)
self.rf_c1_aggr.bn_eps(2e-5)
self.det1 = DETHead()
self.det1.bn_eps(2e-5)
self.head32 = RegressHead()
self.head16 = RegressHead()
self.head8 = RegressHead()
