lizhao/bci_algo
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

replace print with algo_log

Browse Source
This commit is contained in:
lizhao
2026-06-10 16:04:02 +08:00
parent 43adc6fb42
commit ba4ae92647
7 changed files with 33 additions and 33 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
Decoder.py
View File

@@ -14,8 +14,8 @@ from torch.autograd import Variable
# from Device.SunnyLinker import SunnyLinker64 # from Device.SunnyLinker import SunnyLinker64
from SSMVEP.algorithm.tdca import TDCA from SSMVEP.algorithm.tdca import TDCA
from SSMVEP.algorithm.base import generate_cca_references from SSMVEP.algorithm.base import generate_cca_references
from concentration.algorithm.calculate_focus import Calculate # from concentration.algorithm.calculate_focus import Calculate
from blinkdetection.algorithm.eye_detection import blink_detection # from blinkdetection.algorithm.eye_detection import blink_detection
from Zmq.zmqServer import zmqServer from Zmq.zmqServer import zmqServer
from Zmq.zmqClient import zmqClient from Zmq.zmqClient import zmqClient
from MI.Algorithm.conformer_2class import onlineTrain from MI.Algorithm.conformer_2class import onlineTrain
@@ -425,7 +425,7 @@ class Decoder_main(threading.Thread):
algo_log(f"MI运动意图识别: {y_pred}") algo_log(f"MI运动意图识别: {y_pred}")
self.zmqServer.broadcast_message('paradigm', int(y_pred.item())) self.zmqServer.broadcast_message('paradigm', int(y_pred.item()))
end = time.time() end = time.time()
print(f'发送给界面完成,耗时{end - start:.3f}s。') algo_log(f'MI发送给界面完成,耗时{end - start:.3f}s。')
else: # 休息状态 else: # 休息状态
if self.zmqServer.paradigmBuffer.GetDataLenCount() < 25: if self.zmqServer.paradigmBuffer.GetDataLenCount() < 25:
time.sleep(0.005) time.sleep(0.005)

31
MI/Algorithm/conformer_2class.py
View File

@@ -34,7 +34,7 @@ cudnn.benchmark = True
cudnn.deterministic = True cudnn.deterministic = True
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
# writer = SummaryWriter('./TensorBoardX/') # writer = SummaryWriter('./TensorBoardX/')
from logs.log import algo_log
# Convolution module # Convolution module
# use conv to capture local features, instead of postion embedding. # use conv to capture local features, instead of postion embedding.
@@ -318,11 +318,11 @@ class ExP():
train_pred = torch.max(outputs, 1)[1] train_pred = torch.max(outputs, 1)[1]
train_acc = float((train_pred == label).cpu().numpy().astype(int).sum()) / float(label.size(0)) train_acc = float((train_pred == label).cpu().numpy().astype(int).sum()) / float(label.size(0))
print('Epoch:', e, algo_log('Epoch:', e,
' Train loss: %.6f' % loss.detach().cpu().numpy(), ' Train loss: %.6f' % loss.detach().cpu().numpy(),
' Test loss: %.6f' % loss_test.detach().cpu().numpy(), ' Test loss: %.6f' % loss_test.detach().cpu().numpy(),
' Train accuracy %.6f' % train_acc, ' Train accuracy %.6f' % train_acc,
' Test accuracy is %.6f' % acc) ' Test accuracy is %.6f' % acc, level="debug")
self.log_write.write(str(e) + " " + str(acc) + "\n") self.log_write.write(str(e) + " " + str(acc) + "\n")
num = num + 1 num = num + 1
@@ -335,8 +335,8 @@ class ExP():
torch.save(self.model, model_path) torch.save(self.model, model_path)
averAcc = averAcc / num averAcc = averAcc / num
print('The average accuracy is:', averAcc) algo_log('The average accuracy is:', averAcc, level="debug")
print('The best accuracy is:', bestAcc) algo_log('The best accuracy is:', bestAcc, level="debug")
self.log_write.write('The average accuracy is: ' + str(averAcc) + "\n") self.log_write.write('The average accuracy is: ' + str(averAcc) + "\n")
self.log_write.write('The best accuracy is: ' + str(bestAcc) + "\n") self.log_write.write('The best accuracy is: ' + str(bestAcc) + "\n")
@@ -346,10 +346,10 @@ class ExP():
def onlineTrain(data_queue,result_queue): def onlineTrain(data_queue,result_queue):
import torch import torch
print(f"[DEBUG] torch.__version__ = {torch.__version__}") algo_log(f"[DEBUG] torch.__version__ = {torch.__version__}", level="debug")
print(f"[DEBUG] torch.cuda.is_available() = {torch.cuda.is_available()}") algo_log(f"[DEBUG] torch.cuda.is_available() = {torch.cuda.is_available()}", level="debug")
if torch.cuda.is_available(): if torch.cuda.is_available():
print(f"[DEBUG] GPU = {torch.cuda.get_device_name(0)}") algo_log(f"[DEBUG] GPU = {torch.cuda.get_device_name(0)}", level="debug")
try: try:
starttime = datetime.datetime.now() starttime = datetime.datetime.now()
@@ -366,12 +366,12 @@ def onlineTrain(data_queue,result_queue):
data = data_queue.get(timeout=30) data = data_queue.get(timeout=30)
all_data, all_label,model_path,n_chan = data['data'], data['label'],data['modelPath'],data['n_chan'] all_data, all_label,model_path,n_chan = data['data'], data['label'],data['modelPath'],data['n_chan']
exp = ExP(n_chan) exp = ExP(n_chan)
print('训练参数： ',np.shape(all_data),np.shape(all_label),model_path) algo_log('训练参数： ',np.shape(all_data),np.shape(all_label),model_path, level="debug")
bestAcc, averAcc, Y_true, Y_pred = exp.train(all_data,all_label,model_path) bestAcc, averAcc, Y_true, Y_pred = exp.train(all_data,all_label,model_path)
print('THE BEST ACCURACY IS ' + str(bestAcc)) algo_log('THE BEST ACCURACY IS ' + str(bestAcc), level="debug")
endtime = datetime.datetime.now() endtime = datetime.datetime.now()
print('train duration: ',str(endtime - starttime)) algo_log('train duration: ',str(endtime - starttime), level="debug")
# 将模型或参数传回 # 将模型或参数传回
result_queue.put({ result_queue.put({
@@ -387,7 +387,7 @@ def offlineTrain(all_data,all_label,modelPath):
# seed_n = np.random.randint(2025) # seed_n = np.random.randint(2025)
seed_n = 1877 seed_n = 1877
print('seed is ' + str(seed_n)) algo_log('seed is ' + str(seed_n), level="debug")
random.seed(seed_n) random.seed(seed_n)
np.random.seed(seed_n) np.random.seed(seed_n)
torch.manual_seed(seed_n) torch.manual_seed(seed_n)
@@ -397,13 +397,12 @@ def offlineTrain(all_data,all_label,modelPath):
exp = ExP() exp = ExP()
bestAcc, averAcc, Y_true, Y_pred = exp.train(all_data,all_label,modelPath) bestAcc, averAcc, Y_true, Y_pred = exp.train(all_data,all_label,modelPath)
print('THE BEST ACCURACY IS ' + str(bestAcc)) algo_log('THE BEST ACCURACY IS ' + str(bestAcc), level="debug")
endtime = datetime.datetime.now() endtime = datetime.datetime.now()
print('train duration: ',str(endtime - starttime)) algo_log('train duration: ',str(endtime - starttime), level="debug")
if __name__ == "__main__": if __name__ == "__main__":
print(time.asctime(time.localtime(time.time()))) algo_log(f"[DEBUG] time.asctime(time.localtime(time.time())) = {time.asctime(time.localtime(time.time()))}", level="debug")
print(time.asctime(time.localtime(time.time())))

3
MI/Algorithm/conformer_2class_cpu.py
View File

@@ -22,6 +22,7 @@ from einops import rearrange
from einops.layers.torch import Rearrange, Reduce from einops.layers.torch import Rearrange, Reduce
from torch.backends import cudnn from torch.backends import cudnn
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
from logs.log import algo_log
# writer = SummaryWriter('./TensorBoardX/') # writer = SummaryWriter('./TensorBoardX/')
@@ -190,7 +191,7 @@ class ExP():
# 自动选择设备：有 GPU 用 GPU，否则用 CPU # 自动选择设备：有 GPU 用 GPU，否则用 CPU
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# self.device = torch.device("cpu") # self.device = torch.device("cpu")
print(f"Using device: {self.device}") algo_log(f"Using device: {self.device}", level="debug")
# 定义张量类型（不再强制使用 cuda） # 定义张量类型（不再强制使用 cuda）
self.Tensor = torch.FloatTensor self.Tensor = torch.FloatTensor

15
SSVEP/dwfbcca.py
View File

@@ -12,16 +12,17 @@ from scipy.io import loadmat
from scipy.linalg import qr from scipy.linalg import qr
from scipy.signal import filtfilt, lfilter from scipy.signal import filtfilt, lfilter
# from numpy.linalg import _umath_linalg # from numpy.linalg import _umath_linalg
from logs.log import algo_log
class FbccaDw: class FbccaDw:
def __init__(self, fs, num_target, num_chans, num_filter, num_harms, stimTime, parameter, width, winNum,method): def __init__(self, fs, num_target, num_chans, num_filter, num_harms, stimTime, parameter, width, winNum,method):
print('******************************************') algo_log('******************************************', level="debug")
print('parameter list') algo_log('parameter list', level="debug")
print('target:', num_target) algo_log('target:', num_target, level="debug")
print('number of filter bank:', num_filter) algo_log('number of filter bank:', num_filter, level="debug")
print('parameter:', parameter) algo_log('parameter:', parameter, level="debug")
print('width:', width) algo_log('width:', width, level="debug")
self.phase = 0 self.phase = 0
self.bandWidth = width self.bandWidth = width
self.winNum = winNum self.winNum = winNum
@@ -237,7 +238,7 @@ class FbccaDw:
dataFiltered, self.notchZh[0] = lfilter(self.north_b, self.north_a, data, zi=self.notchZh[0]) dataFiltered, self.notchZh[0] = lfilter(self.north_b, self.north_a, data, zi=self.notchZh[0])
return np.asmatrix(dataFiltered) return np.asmatrix(dataFiltered)
except Exception: except Exception:
print(Exception) algo_log(f"Exception: {Exception}", level="debug")
''' '''
getDataQ getDataQ

8
Zmq/zmqServer.py
View File

@@ -2,6 +2,7 @@
import ast import ast
import numpy as np import numpy as np
import threading import threading
import zmq
import json import json
import queue import queue
from typing import Dict from typing import Dict
@@ -13,17 +14,14 @@ from Zmq.filterProcess import FilterRingBuffer
from PubLibrary.InifileHelper import IniRead from PubLibrary.InifileHelper import IniRead
from logs.log import algo_log from logs.log import algo_log
import zmq zmqServer_host = str(IniRead('system', 'zmqServer_host', '127.0.0.1'))
class zmqServer(threading.Thread): class zmqServer(threading.Thread):
def __init__(self, host='0.0.0.0', cmd_port=8099, data_port=8100, device_info=None): def __init__(self, host='0.0.0.0', cmd_port=8099, data_port=8100, device_info=None):
threading.Thread.__init__(self) threading.Thread.__init__(self)
self.device_info = device_info self.device_info = device_info
self.host = host self.host = zmqServer_host
# test_host = "192.168.254.102"
# self.host = test_host
self.cmd_port = cmd_port # 命令交互端口：收JSON命令 + 返JSON结果 self.cmd_port = cmd_port # 命令交互端口：收JSON命令 + 返JSON结果
self.data_port = data_port # 数据交互端口：收二进制原始脑电 + 返二进制滤波结果 self.data_port = data_port # 数据交互端口：收二进制原始脑电 + 返二进制滤波结果

1
config.ini
View File

@@ -21,6 +21,7 @@ Serial_port = COM44
algo_log_level = DEBUG algo_log_level = DEBUG
console_output = 1 console_output = 1
save_train_data = 0 save_train_data = 0
zmqServer_host = 127.0.0.1
; 64 导设备配置 ; 64 导设备配置
[device_type_1] [device_type_1]

2
nuitka_3in1_package.sh
View File

@@ -28,7 +28,7 @@ echo "输出目录：${OUT_DIR}"
python -m nuitka \ python -m nuitka \
--standalone \ --standalone \
--msvc=latest \ --msvc=latest \
--windows-console-mode=force \ --windows-console-mode=disable \
--module-parameter=torch-disable-jit=yes \ --module-parameter=torch-disable-jit=yes \
--enable-plugin=no-qt \ --enable-plugin=no-qt \
--include-package=numpy \ --include-package=numpy \