本文主要是介绍configs，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
configs 部分
```python
import os # 导入os模块，用于系统级操作
emotion = ["Valence"] # 定义情绪列表，只包含情绪维度"Valence"
# 配置参数字典
config = {
"extract_class_label": 1, # 是否提取类别标签
"extract_continuous_label": 1, # 是否提取连续标签
"extract_eeg": 1, # 是否提取EEG数据
"eeg_folder": "eeg", # 存放EEG数据的文件夹名称
"eeg_config": { # EEG数据处理的详细配置
"sampling_frequency": 256, # 采样频率
"window_sec": 2, # 窗口长度（秒）
"hop_sec": 0.25, # 跳跃长度（秒）
"buffer_sec": 5, # 缓冲区长度（秒）
"num_electrodes": 32, # 电极数量
"interest_bands": [(0.3, 4), (4, 8), (8, 12), (12, 18), (18, 30), (30, 45)], # 感兴趣频段
"f_trans_interest_bands": [(0.1, 2), (2, 2), (2, 2), (2, 2), (2, 2), (2, 2)], # 感兴趣频段的过渡频率
"channel_slice": {'eeg': slice(0, 32), 'ecg': slice(32, 35), 'misc': slice(35, -1)}, # 通道切片
"features": ["eeg_bandpower"], # 特征
"filter_type": 'cheby2', # 滤波器类型
"filter_order": 4 # 滤波器阶数
},
"save_npy": 1, # 是否保存为.npy格式的数据
"npy_folder": "compacted_48", # 存放.npy数据的文件夹名称
"dataset_name": "mahnob", # 数据集的名称
"emotion_list": emotion, # 情绪列表
"root_directory": r"D:\DingYi\Dataset\MAHNOB-O", # 原始数据集的根目录路径
"output_root_directory": r"D:\DingYi\Dataset\MAHNOB-P-R", # 处理后数据的输出根目录路径
"raw_data_folder": "Sessions", # 原始数据存放的文件夹名称
"multiplier": { # 不同数据类型的倍增因子
"video": 16,
"eeg_raw": 1,
"eeg_bandpower": 1,
"eeg_DE": 1,
"eeg_RP": 1,
"eeg_Hjorth": 1,
"continuous_label": 1
},
"feature_dimension": { # 不同特征的维度信息
"eeg_raw": (16384,),
"eeg_bandpower": (192,),
"eeg_DE": (192,),
"eeg_RP": (192,),
"eeg_Hjorth": (96,),
"continuous_label": (1,),
"class_label": (1,)
},
"max_epoch": 15, # 最大的训练周期数
"min_epoch": 0, # 最小的训练周期数
"model_name": "2d1d", # 模型的名称
"backbone": { # 模型的骨干网络配置
"state_dict": "res50_ir_0.887",
"mode": "ir"
},
"early_stopping": 10, # 提前停止训练的步数
"load_best_at_each_epoch": 1, # 是否在每个周期加载最佳模型
"time_delay": 0, # 时间延迟
"metrics": ["rmse", "pcc", "ccc"], # 评估指标
"save_plot": 0 # 是否保存图形结果
}
```
这段代码是一个Python字典，包含了各种配置参数，用于处理和分析一个名为MAHNOB的数据集，主要用于情绪识别研究。以下是每行代码的解释：
1. `import os`: 导入Python的os模块，用于操作文件路径等系统级操作。
2. `emotion = ["Valence"]`: 定义一个情绪列表，只包含情绪维度"Valence"。
3. `config = { ... }`: 定义一个名为config的字典，包含了各种配置参数。
4. `"extract_class_label": 1`: 是否提取类别标签，这里设为1表示是。
5. `"extract_continuous_label": 1`: 是否提取连续标签，这里设为1表示是。
6. `"extract_eeg": 1`: 是否提取EEG数据，这里设为1表示是。
7. `"eeg_folder": "eeg"`: 存放EEG数据的文件夹名称。
8. `"eeg_config": { ... }`: EEG数据处理的详细配置，包括采样频率、窗口长度、跳跃长度、通道数量等参数。
9. `"save_npy": 1`: 是否保存为.npy格式的数据，这里设为1表示是。
10. `"npy_folder": "compacted_48"`: 存放.npy数据的文件夹名称。
11. `"dataset_name": "mahnob"`: 数据集的名称。
12. `"emotion_list": emotion`: 情绪列表，使用了之前定义的emotion变量。
13. `"root_directory": r"D:\DingYi\Dataset\MAHNOB-O"`: 原始数据集的根目录路径。
14. `"output_root_directory": r"D:\DingYi\Dataset\MAHNOB-P-R"`: 处理后数据的输出根目录路径。
15. `"raw_data_folder": "Sessions"`: 原始数据存放的文件夹名称。
16. `"multiplier": { ... }`: 不同数据类型的倍增因子，用于数据增强或者调整数据量。
17. `"feature_dimension": { ... }`: 不同特征的维度信息，用于数据处理和模型输入。
18. `"max_epoch": 15`: 最大的训练周期数。
19. `"min_epoch": 0`: 最小的训练周期数。
20. `"model_name": "2d1d"`: 模型的名称，这里只是命名用途，实际上没有使用。
21. `"backbone": { ... }`: 模型的骨干网络配置，包括状态字典和模式。
22. `"early_stopping": 10`: 提前停止训练的步数。
23. `"load_best_at_each_epoch": 1`: 是否在每个周期加载最佳模型。
24. `"time_delay": 0`: 时间延迟，用于连续标签在数据点中的移动。
25. `"metrics": ["rmse", "pcc", "ccc"]`: 评估指标，包括均方根误差、皮尔逊相关系数和一致性相关系数。
26. `"save_plot": 0`: 是否保存图形结果，这里设为0表示否。
这些配置参数用于设置数据预处理、模型训练和评估过程中的各种选项和参数，确保流程能够顺利进行和有效执行。
from base.preprocessing import GenericDataPreprocessing # 导入基础数据预处理类
from base.utils import expand_index_by_multiplier, load_pickle, save_to_pickle, get_filename_from_a_folder_given_extension, ensure_dir # 导入一些辅助函数和工具
from base.label_config import * # 导入标签配置
import os # 导入os模块，用于系统级操作
import scipy.io as sio # 导入scipy.io模块，用于读取.mat文件
import pandas as pd # 导入pandas库，用于数据处理和分析
import numpy as np # 导入numpy库，用于数值计算
import xml.etree.ElementTree as et # 导入xml.etree.ElementTree模块，用于解析XML文件
generate_dataset.py
from base.preprocessing import GenericDataPreprocessing # 导入基础数据预处理类
from base.utils import expand_index_by_multiplier, load_pickle, save_to_pickle, get_filename_from_a_folder_given_extension, ensure_dir # 导入一些辅助函数和工具
from base.label_config import * # 导入标签配置
import os # 导入os模块，用于系统级操作
import scipy.io as sio # 导入scipy.io模块，用于读取.mat文件
import pandas as pd # 导入pandas库，用于数据处理和分析
import numpy as np # 导入numpy库，用于数值计算
import xml.etree.ElementTree as et # 导入xml.etree.ElementTree模块，用于解析XML文件
class Preprocessing(GenericDataPreprocessing):
def __init__(self, config):
super().__init__(config)
def generate_iterator(self):
# 生成迭代器，返回按照文件名排序的文件路径列表
path = os.path.join(self.config['root_directory'], self.config['raw_data_folder'])
iterator = [os.path.join(path, file) for file in sorted(os.listdir(path), key=float)]
return iterator
def generate_per_trial_info_dict(self):
# 生成每个试验的信息字典
per_trial_info_path = os.path.join(self.config['output_root_directory'], "processing_records.pkl")
if os.path.isfile(per_trial_info_path):
per_trial_info = load_pickle(per_trial_info_path)
else:
per_trial_info = {}
pointer = 0
sub_trial_having_continuous_label = self.get_sub_trial_info_for_continuously_labeled()
all_continuous_labels = self.read_all_continuous_label()
iterator = self.generate_iterator()
for idx, file in enumerate(iterator):
kwargs = {}
this_trial = {}
print(file)
time_stamp_file = get_filename_from_a_folder_given_extension(file, "tsv", "All-Data")[0]
video_trim_range = self.read_start_end_from_mahnob_tsv(time_stamp_file)
if video_trim_range is not None:
this_trial['video_trim_range'] = self.read_start_end_from_mahnob_tsv(time_stamp_file)
else:
this_trial['discard'] = 1
continue
this_trial['has_continuous_label'] = 0
session = int(file.split(os.sep)[-1])
subject_no, trial_no = session // 130 + 1, session % 130
if subject_no == sub_trial_having_continuous_label[pointer][0] and trial_no == sub_trial_having_continuous_label[pointer][1]:
this_trial['has_continuous_label'] = 1
this_trial['continuous_label'] = None
this_trial['annotated_index'] = None
annotated_index = np.arange(this_trial['video_trim_range'][0][1])
if this_trial['has_continuous_label']:
raw_continuous_label = all_continuous_labels[pointer]
this_trial['continuous_label'] = raw_continuous_label
annotated_index = self.process_continuous_label(raw_continuous_label)
this_trial['annotated_index'] = annotated_index
pointer += 1
this_trial['has_eeg'] = 1
eeg_path = get_filename_from_a_folder_given_extension(file, "bdf")
if len(eeg_path) == 1:
this_trial['eeg_path'] = eeg_path[0].split(os.sep)
else:
this_trial['eeg_path'] = None
this_trial['has_eeg'] = 0
this_trial['audio_path'] = ""
this_trial['subject_no'] = subject_no
this_trial['trial_no'] = trial_no
this_trial['trial'] = "P{}-T{}".format(str(subject_no), str(trial_no))
this_trial['target_fps'] = 64
kwargs['feature'] = "video"
kwargs['has_continuous_label'] = this_trial['has_continuous_label']
this_trial['video_annotated_index'] = self.get_annotated_index(annotated_index, **kwargs)
this_trial['class_label'] = get_filename_from_a_folder_given_extension(file, "xml")[0]
per_trial_info[idx] = this_trial
ensure_dir(per_trial_info_path)
save_to_pickle(per_trial_info_path, per_trial_info)
self.per_trial_info = per_trial_info
def generate_dataset_info(self):
# 生成数据集信息
class_label = {}
for idx, record in self.per_trial_info.items():
self.dataset_info['trial'].append(record['processing_record']['trial'])
self.dataset_info['trial_no'].append(record['trial_no'])
self.dataset_info['subject_no'].append(record['subject_no'])
self.dataset_info['has_continuous_label'].append(record['has_continuous_label'])
self.dataset_info['has_eeg'].append(record['has_eeg'])
if record['has_continuous_label']:
self.dataset_info['length'].append(len(record['continuous_label']))
else:
self.dataset_info['length'].append(len(record['video_annotated_index']) // 16)
if self.config['extract_class_label']:
class_label.update({record['processing_record']['trial']: self.extract_class_label_fn(record)})
self.dataset_info['multiplier'] = self.config['multiplier']
self.dataset_info['data_folder'] = self.config['npy_folder']
path = os.path.join(self.config['output_root_directory'], 'dataset_info.pkl')
save_to_pickle(path, self.dataset_info)
if self.config['extract_class_label']:
path = os.path.join(self.config['output_root_directory'], 'class_label.pkl')
save_to_pickle(path, class_label)
def extract_class_label_fn(self, record):
# 提取类别标签
class_label = {}
if record['has_eeg']:
xml_file = et.parse(record['class_label']).getroot()
felt_emotion = xml_file.find('.').attrib['feltEmo']
felt_arousal = xml_file.find('.').attrib['feltArsl']
felt_valence = xml_file.find('.').attrib['feltVlnc']
arousal = 0 if float(felt_arousal) <= 5 else 1
valence = 0 if float(felt_valence) <= 5 else 1
class_label = {
"Arousal": arousal,
"Valence": valence,
"Arousal_3cls": arousal_class_to_number[emotion_tag_to_arousal_class[number_to_emotion_tag_dict[felt_emotion]]],
"Valence_3cls": valence_class_to_number[emotion_tag_to_valence_class[number_to_emotion_tag_dict[felt_emotion]]]
}
return class_label
def extract_continuous_label_fn(self, idx, npy_folder):
# 提取连续标签
if self.per_trial_info[idx]["has_continuous_label"]:
raw_continuous_label = self.per_trial_info[idx]['continuous_label']
if self.config['save_npy']:
filename = os.path.join(npy_folder, "continuous_label.npy")
if not os.path.isfile(filename):
ensure_dir(filename)
np.save(filename, raw_continuous_label)
def load_continuous_label(self, path, **kwargs):
# 加载连续标签
cols = [emotion.lower() for emotion in self.config['emotion_list']]
if os.path.isfile(path):
continuous_label = pd.read_csv(path, sep=";",
skipinitialspace=True, usecols=cols,
index_col=False).values.squeeze()
else:
continuous_label = 0
return continuous_label
def get_annotated_index(self, annotated_index, **kwargs):
# 获取标注索引
feature = kwargs['feature']
multiplier = self.config['multiplier'][feature]
if kwargs['has_continuous_label']:
annotated_index = expand_index_by_multiplier(annotated_index, multiplier)
else:
pass
return annotated_index
def get_sub_trial_info_for_continuously_labeled(self):
# 获取连续标签的子试验信息
label_file = os.path.join(self.config['root_directory'], "lable_continous_Mahnob.mat")
mat_content = sio.loadmat(label_file)
sub_trial_having_continuous_label = mat_content['trials_included']
return sub_trial_having_continuous_label
@staticmethod
def read_start_end_from_mahnob_tsv(tsv_file):
# 从Mahnob的tsv文件中读取起始和结束时间
if os.path.isfile(tsv_file):
data = pd.read_csv(tsv_file, sep='\t', skiprows=23)
end = data[data['Event'] == 'MovieEnd'].index[0]
start_end = [(0, end)]
else:
start_end = None
return start_end
def read_all_continuous_label(self):
# 读取所有连续标签
label_file = os.path.join(self.config['root_directory'], "lable_continous_Mahnob.mat")
mat_content = sio.loadmat(label_file)
annotation_cell = np.squeeze(mat_content['labels'])
label_list = []
for index in range(len(annotation_cell)):
label_list.append(annotation_cell[index].T)
return label_list
@staticmethod
def init_dataset_info():
# 初始化数据集信息
dataset_info = {
"trial": [],
"subject_no": [],
"trial_no": [],
"length": [],
"has_continuous_label": [],
"has_eeg": [],
}
return dataset_info
if __name__ == "__main__":
from configs import config
pre = Preprocessing(config)
pre.generate_per_trial_info_dict()
pre.prepare_data()
这段代码定义了一个名为Preprocessing的类，继承自GenericDataPreprocessing类，用于数据预处理。它包含了一些方法和函数，用于生成每个试验的信息字典、生成数据集信息、提取类别标签、提取连续标签等操作。在if __name__ == "__main__":部分，创建了Preprocessing对象，并调用了相关方法进行数据预处理。
main.py
from base.preprocessing import GenericDataPreprocessing # 导入自定义的GenericDataPreprocessing类
from base.utils import expand_index_by_multiplier, load_pickle, save_to_pickle, get_filename_from_a_folder_given_extension, ensure_dir # 导入一些辅助函数和工具
from base.label_config import * # 导入标签配置
import os # 导入os模块，用于文件和目录操作
import scipy.io as sio # 导入scipy.io模块，用于读取MATLAB文件
import pandas as pd # 导入pandas库，用于数据处理
import numpy as np # 导入numpy库，用于数值计算
import xml.etree.ElementTree as et # 导入xml.etree.ElementTree模块，用于解析XML文件
class Preprocessing(GenericDataPreprocessing):
def __init__(self, config):
super().__init__(config)
def generate_iterator(self):
path = os.path.join(self.config['root_directory'], self.config['raw_data_folder'])
iterator = [os.path.join(path, file) for file in sorted(os.listdir(path), key=float)]
return iterator
def generate_per_trial_info_dict(self):
# 生成每个试验的信息字典
per_trial_info_path = os.path.join(self.config['output_root_directory'], "processing_records.pkl")
if os.path.isfile(per_trial_info_path):
per_trial_info = load_pickle(per_trial_info_path)
else:
per_trial_info = {}
pointer = 0
sub_trial_having_continuous_label = self.get_sub_trial_info_for_continuously_labeled()
all_continuous_labels = self.read_all_continuous_label()
iterator = self.generate_iterator()
for idx, file in enumerate(iterator):
kwargs = {}
this_trial = {}
print(file)
time_stamp_file = get_filename_from_a_folder_given_extension(file, "tsv", "All-Data")[0]
video_trim_range = self.read_start_end_from_mahnob_tsv(time_stamp_file)
if video_trim_range is not None:
this_trial['video_trim_range'] = self.read_start_end_from_mahnob_tsv(time_stamp_file)
else:
this_trial['discard'] = 1
continue
this_trial['has_continuous_label'] = 0
session = int(file.split(os.sep)[-1])
subject_no, trial_no = session // 130 + 1, session % 130
if subject_no == sub_trial_having_continuous_label[pointer][0] and trial_no == sub_trial_having_continuous_label[pointer][1]:
this_trial['has_continuous_label'] = 1
this_trial['continuous_label'] = None
this_trial['annotated_index'] = None
annotated_index = np.arange(this_trial['video_trim_range'][0][1])
if this_trial['has_continuous_label']:
raw_continuous_label = all_continuous_labels[pointer]
this_trial['continuous_label'] = raw_continuous_label
annotated_index = self.process_continuous_label(raw_continuous_label)
this_trial['annotated_index'] = annotated_index
pointer += 1
this_trial['has_eeg'] = 1
eeg_path = get_filename_from_a_folder_given_extension(file, "bdf")
if len(eeg_path) == 1:
this_trial['eeg_path'] = eeg_path[0].split(os.sep)
else:
this_trial['eeg_path'] = None
this_trial['has_eeg'] = 0
this_trial['audio_path'] = ""
this_trial['subject_no'] = subject_no
this_trial['trial_no'] = trial_no
this_trial['trial'] = "P{}-T{}".format(str(subject_no), str(trial_no))
this_trial['target_fps'] = 64
kwargs['feature'] = "video"
kwargs['has_continuous_label'] = this_trial['has_continuous_label']
this_trial['video_annotated_index'] = self.get_annotated_index(annotated_index, **kwargs)
this_trial['class_label'] = get_filename_from_a_folder_given_extension(file, "xml")[0]
per_trial_info[idx] = this_trial
ensure_dir(per_trial_info_path)
save_to_pickle(per_trial_info_path, per_trial_info)
self.per_trial_info = per_trial_info
def generate_dataset_info(self):
# 生成数据集信息
class_label = {}
for idx, record in self.per_trial_info.items():
self.dataset_info['trial'].append(record['processing_record']['trial'])
self.dataset_info['trial_no'].append(record['trial_no'])
self.dataset_info['subject_no'].append(record['subject_no'])
self.dataset_info['has_continuous_label'].append(record['has_continuous_label'])
self.dataset_info['has_eeg'].append(record['has_eeg'])
if record['has_continuous_label']:
self.dataset_info['length'].append(len(record['continuous_label']))
else:
self.dataset_info['length'].append(len(record['video_annotated_index']) // 16)
if self.config['extract_class_label']:
class_label.update({record['processing_record']['trial']: self.extract_class_label_fn(record)})
self.dataset_info['multiplier'] = self.config['multiplier']
self.dataset_info['data_folder'] = self.config['npy_folder']
path = os.path.join(self.config['output_root_directory'], 'dataset_info.pkl')
save_to_pickle(path, self.dataset_info)
if self.config['extract_class_label']:
path = os.path.join(self.config['output_root_directory'], 'class_label.pkl')
save_to_pickle(path, class_label)
def extract_class_label_fn(self, record):
# 提取类别标签的函数
class_label = {}
if record['has_eeg']:
xml_file = et.parse(record['class_label']).getroot()
felt_emotion = xml_file.find('.').attrib['feltEmo']
felt_arousal = xml_file.find('.').attrib['feltArsl']
felt_valence = xml_file.find('.').attrib['feltVlnc']
arousal = 0 if float(felt_arousal) <= 5 else 1
valence = 0 if float(felt_valence) <= 5 else 1
class_label = {
"Arousal": arousal,
"Valence": valence,
"Arousal_3cls": arousal_class_to_number[emotion_tag_to_arousal_class[number_to_emotion_tag_dict[felt_emotion]]],
"Valence_3cls": valence_class_to_number[emotion_tag_to_valence_class[number_to_emotion_tag_dict[felt_emotion]]]
}
return class_label
def extract_continuous_label_fn(self, idx, npy_folder):
# 提取连续标签的函数
if self.per_trial_info[idx]["has_continuous_label"]:
raw_continuous_label = self.per_trial_info[idx]['continuous_label']
if self.config['save_npy']:
filename = os.path.join(npy_folder, "continuous_label.npy")
if not os.path.isfile(filename):
ensure_dir(filename)
np.save(filename, raw_continuous_label)
def load_continuous_label(self, path, **kwargs):
# 加载连续标签
cols = [emotion.lower() for emotion in self.config['emotion_list']]
if os.path.isfile(path):
continuous_label = pd.read_csv(path, sep=";",
skipinitialspace=True, usecols=cols,
index_col=False).values.squeeze()
else:
continuous_label = 0
return continuous_label
def get_annotated_index(self, annotated_index, **kwargs):
# 获取标注索引
feature = kwargs['feature']
multiplier = self.config['multiplier'][feature]
if kwargs['has_continuous_label']:
annotated_index = expand_index_by_multiplier(annotated_index, multiplier)
else:
pass
return annotated_index
def get_sub_trial_info_for_continuously_labeled(self):
# 获取具有连续标签的子试验信息
label_file = os.path.join(self.config['root_directory'], "lable_continous_Mahnob.mat")
mat_content = sio.loadmat(label_file)
sub_trial_having_continuous_label = mat_content['trials_included']
return sub_trial_having_continuous_label
@staticmethod
def read_start_end_from_mahnob_tsv(tsv_file):
# 从Mahnob TSV文件中读取起始和结束时间
if os.path.isfile(tsv_file):
data = pd.read_csv(tsv_file, sep='\t', skiprows=23)
end = data[data['Event'] == 'MovieEnd'].index[0]
start_end = [(0, end)]
else:
start_end = None
return start_end
def read_all_continuous_label(self):
# 读取所有连续标签
label_file = os.path.join(self.config['root_directory'], "lable_continous_Mahnob.mat")
mat_content = sio.loadmat(label_file)
annotation_cell = np.squeeze(mat_content['labels'])
label_list = []
for index in range(len(annotation_cell)):
label_list.append(annotation_cell[index].T)
return label_list
@staticmethod
def init_dataset_info():
# 初始化数据集信息
dataset_info = {
"trial": [],
"subject_no": [],
"trial_no": [],
"length": [],
"has_continuous_label": [],
"has_eeg": [],
}
return dataset_info
if __name__ == "__main__":
from configs import config # 导入配置文件
pre = Preprocessing(config) # 创建Preprocessing对象，传入配置文件
pre.generate_per_trial_info_dict() # 生成每个试验的信息字典
pre.prepare_data() # 准备数据
这段代码是一个数据预处理的类Preprocessing，继承自GenericDataPreprocessing。它包含了一些方法用于生成每个试验的信息字典、生成数据集信息、提取类别标签和连续标签等操作。在__main__函数中，创建了一个Preprocessing对象，并调用了相关方法进行数据预处理。
加入其他数据集中
```python
from model import MASA_TCN # 从model模块中导入MASA_TCN模型
data = torch.randn(1, 1, 192, 96) # 生成一个随机张量作为输入数据，形状为(batch_size=1, cnn_channel=1, EEG_channel*feature=32*6, data_sequence=96)
# 对于回归任务，输出形状为(batch_size, data_sequence, 1)。
net = MASA_TCN(
cnn1d_channels=[128, 128, 128], # 1维卷积层的通道数列表
cnn1d_kernel_size=[3, 5, 15], # 1维卷积层的核大小列表
cnn1d_dropout_rate=0.1, # 1维卷积层的dropout率
num_eeg_chan=32, # EEG通道数
freq=6, # 特征频率
output_dim=1, # 输出维度
early_fusion=True, # 是否使用早期融合
model_type='reg') # 模型类型为回归
preds = net(data) # 对输入数据进行预测
# 对于分类任务，输出形状为(batch_size, num_classes)。注意：output_dim应该是类别的数量。
net = MASA_TCN(
cnn1d_channels=[128, 128, 128], # 1维卷积层的通道数列表
cnn1d_kernel_size=[3, 5, 15], # 1维卷积层的核大小列表
cnn1d_dropout_rate=0.1, # 1维卷积层的dropout率
num_eeg_chan=32, # EEG通道数
freq=6, # 特征频率
output_dim=2, # 输出维度
early_fusion=True, # 是否使用早期融合
model_type='cls') # 模型类型为分类
preds = net(data) # 对输入数据进行预测
```
这段代码首先导入了MASA_TCN模型，然后创建了一个随机输入数据，并使用MASA_TCN模型进行了回归和分类任务的预测。注释已经在代码中添加了。
这篇关于configs的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
