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, out, stride):
self.c1 = M.DWConvLayer(5, 1, stride=stride, usebias=False)
self.c2 = M.ConvLayer(1, out, usebias=False, batch_norm=True)
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()
