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()