lab_dict=np.load(class_dict_file).item()
print(CNN_net.out_dim)
DNN1_arch = {'input_dim': CNN_net.out_dim,
'fc_lay': fc_lay,
'fc_drop': fc_drop,
'fc_use_batchnorm': fc_use_batchnorm,
'fc_use_laynorm': fc_use_laynorm,
'fc_use_laynorm_inp': fc_use_laynorm_inp,
'fc_use_batchnorm_inp':fc_use_batchnorm_inp,
'fc_act': fc_act,
}
DNN1_net=MLP(DNN1_arch)
DNN1_net.cuda()
DNN2_arch = {'input_dim':fc_lay[-1] ,
'fc_lay': class_lay,
'fc_drop': class_drop,
'fc_use_batchnorm': class_use_batchnorm,
'fc_use_laynorm': class_use_laynorm,
'fc_use_laynorm_inp': class_use_laynorm_inp,
'fc_use_batchnorm_inp':class_use_batchnorm_inp,
'fc_act': class_act,
}
DNN2_net=MLP(DNN2_arch)
DNN2_net.cuda()
lab_dict = np.load(class_dict_file, allow_pickle=True).item()
DNN1_arch = {'input_dim': CNN_net_out_dim,
'fc_lay': fc_lay,
'fc_drop': fc_drop,
'fc_use_batchnorm': fc_use_batchnorm,
'fc_use_laynorm': fc_use_laynorm,
'fc_use_laynorm_inp': fc_use_laynorm_inp,
'fc_use_batchnorm_inp': fc_use_batchnorm_inp,
'fc_act': fc_act,
}
DNN1_net = MLP(DNN1_arch)
if IS_DATA_PARALLEL:
DNN1_net = nn.DataParallel(DNN1_net, device_ids=DEVICE_IDS)
DNN1_net.cuda(device)
DNN2_arch = {'input_dim': fc_lay[-1],
'fc_lay': class_lay,
'fc_drop': class_drop,
'fc_use_batchnorm': class_use_batchnorm,
'fc_use_laynorm': class_use_laynorm,
'fc_use_laynorm_inp': class_use_laynorm_inp,
'fc_use_batchnorm_inp': class_use_batchnorm_inp,
'fc_act': class_act,
}
DNN2_net = MLP(DNN2_arch)
if IS_DATA_PARALLEL:
DNN2_net = nn.DataParallel(DNN2_net, device_ids=DEVICE_IDS)
DNN2_net.cuda(device)
