BP4D_EAC_dlib_train.py 文件源码

def build_model():
    net = {}
    net['input'] = InputLayer((None, 512*20, 3, 3))

    au_fc_layers=[]
    for i in range(20):
        net['roi_AU_N_'+str(i)]=SliceLayer(net['input'],indices=slice(i*512,(i+1)*512),axis=1)

        #try to adding upsampling here for more conv

        net['Roi_upsample_'+str(i)]=Upscale2DLayer(net['roi_AU_N_'+str(i)],scale_factor=2)

        net['conv_roi_'+str(i)]=ConvLayer(net['Roi_upsample_'+str(i)],512,3)

        net['au_fc_'+str(i)]=DenseLayer(net['conv_roi_'+str(i)],num_units=150)

        au_fc_layers+=[net['au_fc_'+str(i)]]

    #
    net['local_fc']=concat(au_fc_layers)
    net['local_fc2']=DenseLayer(net['local_fc'],num_units=2048)

    net['local_fc_dp']=DropoutLayer(net['local_fc2'],p=0.5)


    # net['fc_comb']=concat([net['au_fc_layer'],net['local_fc_dp']])


    # net['fc_dense']=DenseLayer(net['fc_comb'],num_units=1024)

    # net['fc_dense_dp']=DropoutLayer(net['fc_dense'],p=0.3)

    net['real_out']=DenseLayer(net['local_fc_dp'],num_units=12,nonlinearity=sigmoid)


    # net['final']=concat([net['pred_pos_layer'],net['output_layer']])

    return net