replace print with algo_log

Decoder.py

@@ -14,8 +14,8 @@ from torch.autograd import Variable
 # from Device.SunnyLinker import SunnyLinker64
 from SSMVEP.algorithm.tdca import TDCA
 from SSMVEP.algorithm.base import generate_cca_references
-from concentration.algorithm.calculate_focus import Calculate
-from blinkdetection.algorithm.eye_detection import blink_detection
+# from concentration.algorithm.calculate_focus import Calculate
+# from blinkdetection.algorithm.eye_detection import blink_detection
 from Zmq.zmqServer import zmqServer
 from Zmq.zmqClient import zmqClient
 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}")
             self.zmqServer.broadcast_message('paradigm', int(y_pred.item()))
             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
             if self.zmqServer.paradigmBuffer.GetDataLenCount() < 25:
                 time.sleep(0.005)

MI/Algorithm/conformer_2class.py

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

MI/Algorithm/conformer_2class_cpu.py

@@ -22,6 +22,7 @@ from einops import rearrange
 from einops.layers.torch import Rearrange, Reduce
 from torch.backends import cudnn
 from sklearn.model_selection import train_test_split
+from logs.log import algo_log
 # writer = SummaryWriter('./TensorBoardX/')
 
 
@@ -190,7 +191,7 @@ class ExP():
         # 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("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）
         self.Tensor = torch.FloatTensor

SSVEP/dwfbcca.py

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

Zmq/zmqServer.py

@@ -2,6 +2,7 @@
 import ast
 import numpy as np
 import threading
+import zmq
 import json
 import queue
 from typing import Dict
@@ -13,17 +14,14 @@ from Zmq.filterProcess import FilterRingBuffer
 from PubLibrary.InifileHelper import IniRead
 from logs.log import algo_log
 
-import zmq
+zmqServer_host = str(IniRead('system', 'zmqServer_host', '127.0.0.1'))
 
 class zmqServer(threading.Thread):
     def __init__(self, host='0.0.0.0', cmd_port=8099, data_port=8100, device_info=None):
         threading.Thread.__init__(self)
         self.device_info = device_info
         
-        self.host = host
-        
-        # test_host = "192.168.254.102"
-        # self.host = test_host
+        self.host = zmqServer_host
         
         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

config.ini

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

nuitka_3in1_package.sh

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