分析代码hydramainconfig_name=configdef mainconf DictConfig if not ospathisdirconfpathfeat_path osmakedirsconfpathfeat_path if not ospathisdirconfpathfeat_train osmakedirsconfpathfeat_

1. 创建main函数并读取配置文件：

@hydra.main(config_name="config") def main(conf : DictConfig):

2. 创建特征保存路径：

if not os.path.isdir(conf.path.feat_path): os.makedirs(conf.path.feat_path)

3. 创建训练集特征保存路径：

if not os.path.isdir(conf.path.feat_train): os.makedirs(conf.path.feat_train)

4. 创建验证集特征保存路径：

if not os.path.isdir(conf.path.feat_eval): os.makedirs(conf.path.feat_eval)

5. 如果需要进行特征提取，则执行以下代码：

if conf.set.features:

    print(" --Feature Extraction Stage--")
    Num_extract_train,data_shape = feature_transform(conf=conf,mode="train")
    print("Shape of dataset is {}".format(data_shape))
    print("Total training samples is {}".format(Num_extract_train))

    Num_extract_eval = feature_transform(conf=conf,mode='eval')
    print("Total number of samples used for evaluation: {}".format(Num_extract_eval))
    print(" --Feature Extraction Complete--")

6. 如果需要进行模型训练，则执行以下代码：

if conf.set.train:

    if not os.path.isdir(conf.path.Model):
        os.makedirs(conf.path.Model)

    init_seed()


    gen_train = Datagen(conf)
    X_train,Y_train,X_val,Y_val = gen_train.generate_train()
    X_tr = torch.tensor(X_train)
    Y_tr = torch.LongTensor(Y_train)
    X_val = torch.tensor(X_val)
    Y_val = torch.LongTensor(Y_val)

    samples_per_cls =  conf.train.n_shot * 2

    batch_size_tr = samples_per_cls * conf.train.k_way
    batch_size_vd = batch_size_tr
    
    if conf.train.num_episodes is not None:

        num_episodes_tr = conf.train.num_episodes
        num_episodes_vd = conf.train.num_episodes

    else:

        num_episodes_tr = len(Y_train)//batch_size_tr
        num_episodes_vd = len(Y_val)//batch_size_vd

    samplr_train = EpisodicBatchSampler(Y_train,num_episodes_tr,conf.train.k_way,samples_per_cls)
    samplr_valid = EpisodicBatchSampler(Y_val,num_episodes_vd,conf.train.k_way,samples_per_cls)

    train_dataset = torch.utils.data.TensorDataset(X_tr,Y_tr)
    valid_dataset = torch.utils.data.TensorDataset(X_val,Y_val)

    train_loader = torch.utils.data.DataLoader(dataset=train_dataset,batch_sampler=samplr_train,num_workers=0,pin_memory=True,shuffle=False)
    valid_loader = torch.utils.data.DataLoader(dataset=valid_dataset,batch_sampler=samplr_valid,num_workers=0,pin_memory=True,shuffle=False)

    if conf.train.encoder == 'Resnet':
        encoder  = ResNet()
    else:
        encoder = ProtoNet()

    
    best_acc,model = train_protonet(encoder,train_loader,valid_loader,conf,num_episodes_tr,num_episodes_vd)
    print("Best accuracy of the model on training set is {}".format(best_acc))

7. 如果需要进行模型评估，则执行以下代码：

if conf.set.eval:

    device = 'cuda'

    init_seed()


    name_arr = np.array([])
    onset_arr = np.array([])
    offset_arr = np.array([])
    all_feat_files = [file for file in glob(os.path.join(conf.path.feat_eval,'*.h5'))]

    for feat_file in all_feat_files:
        feat_name = feat_file.split('/')[-1]
        audio_name = feat_name.replace('h5','wav')

        print("Processing audio file : {}".format(audio_name))

        hdf_eval = h5py.File(feat_file,'r')
        strt_index_query =  hdf_eval['start_index_query'][:][0]
        
        
        onset,offset = evaluate_prototypes(conf,hdf_eval,device,strt_index_query)

        name = np.repeat(audio_name,len(onset))
        name_arr = np.append(name_arr,name)
        onset_arr = np.append(onset_arr,onset)
        offset_arr = np.append(offset_arr,offset)

    df_out = pd.DataFrame({'Audiofilename':name_arr,'Starttime':onset_arr,'Endtime':offset_arr})
    csv_path = os.path.join(conf.path.root_dir,'Eval_out.csv')
    df_out.to_csv(csv_path,index=False