打标epoch_finished, event_inner_index,epoch data 走队列
This commit is contained in:
Ivey Song
111
Decoder.py
111
Decoder.py
@@ -9,6 +9,7 @@ import numpy as np
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import time
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import torch
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from queue import Empty
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import queue
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from scipy import signal
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from torch.autograd import Variable
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# from Device.SunnyLinker import SunnyLinker64
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@@ -276,32 +277,28 @@ class Decoder_main(threading.Thread):
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'''训练阶段采集数据'''
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if self.zmqServer.state_mode == 'train': # 训练状态
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if self.zmqServer.pack_contain_event:
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with self.zmqServer.paradigmBufferLock:
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self.zmqServer.paradigmBuffer.resetAllPara()
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self.zmqServer.pack_contain_event = False
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if self.zmqServer.epoch_finished:
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data_length = self.zmqServer.paradigmBuffer.GetDataLenCount()
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if data_length >= self.train_epoch[1] + self.zmqServer.event_inner_idx:
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self.currentLabel = self.zmqServer.currentLabel
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trainTrial = self.zmqServer.paradigmBuffer.get_SSMVEPData() # 取出所有数据
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algo_log(f"取出的：{trainTrial.shape}，event：{trainTrial[-2, self.zmqServer.event_inner_idx]}", level="DEBUG")
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try:
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epoch_payload = self.zmqServer.epoch_queue.get_nowait()
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except queue.Empty:
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time.sleep(0.0001)
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return
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trainTrial = epoch_payload['snapshot']
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event_inner_idx = epoch_payload['event_inner_idx']
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self.currentLabel = epoch_payload['currentLabel']
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data_length = epoch_payload['data_length']
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if trainTrial is None or data_length < self.train_epoch[1] + event_inner_idx:
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algo_log(f"SSMVEP epoch数据长度不足: {data_length}, 跳过", level="WARNING")
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return
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algo_log(f"取出的：{trainTrial.shape}，event：{trainTrial[-2, event_inner_idx]}", level="DEBUG")
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trainTrial = self.preprocess(trainTrial[:self.n_chan, :]) # 预处理
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trainTrial = trainTrial[:, self.zmqServer.event_inner_idx + self.train_epoch[
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0]:self.zmqServer.event_inner_idx + self.train_epoch[1]]
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trainTrial = trainTrial[:, event_inner_idx + self.train_epoch[
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0]:event_inner_idx + self.train_epoch[1]]
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if trainTrial.shape[1] == (self.train_epoch[1] - self.train_epoch[0]) and isinstance(
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self.trainLabel, list) \
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and self.trainLabel.count(self.currentLabel) < self.single_train:
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self.trainData.append(trainTrial)
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self.trainLabel.append(self.currentLabel)
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algo_log(f"SSMVEP训练集：{np.shape(self.trainData)}", level="DEBUG")
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else:
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algo_log(f"epoch_finished {self.zmqServer.epoch_finished}, 数据长度不足 {data_length}", level="DEBUG")
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self.zmqServer.epoch_finished = False
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else:
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time.sleep(0.001)
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return
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elif self.zmqServer.state_mode == 'predict': # 测试状态
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if self.load_model == False: # 模型尚未训练完成
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@@ -313,18 +310,22 @@ class Decoder_main(threading.Thread):
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now = datetime.now()
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formatted_time = now.strftime('%H:%M:%S.%f')[:-3]
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algo_log(f"SSMVEP模型启动预测 {formatted_time}", level="DEBUG")
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if self.zmqServer.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \
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self.interval_epoch[1] \
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+ self.zmqServer.event_inner_idx:
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# algo_log(f"SSMVEP模型启动预测 {self.zmqServer.epoch_finished}", level="DEBUG")
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try:
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epoch_payload = self.zmqServer.epoch_queue.get_nowait()
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except queue.Empty:
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time.sleep(0.0001)
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return
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data = self.zmqServer.paradigmBuffer.get_SSMVEPData() # 读取全部数据
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algo_log(f"取出的：{data.shape}, event: {data[-2, self.zmqServer.event_inner_idx]}", level="DEBUG")
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data = epoch_payload['snapshot']
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event_inner_idx = epoch_payload['event_inner_idx']
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data_length = epoch_payload['data_length']
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if data is None or data_length < self.interval_epoch[1] + event_inner_idx:
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algo_log(f"SSMVEP predict epoch数据长度不足: {data_length}, 跳过", level="WARNING")
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return
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algo_log(f"取出的：{data.shape}, event: {data[-2, event_inner_idx]}", level="DEBUG")
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data = self.preprocess(data[:self.n_chan, :]) # 预处理
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data = data[:,
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self.zmqServer.event_inner_idx + self.interval_epoch[
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0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]]
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event_inner_idx + self.interval_epoch[
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0]:event_inner_idx + self.interval_epoch[1]]
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pad_eeg_test = np.zeros(
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(data.shape[0], int((self.sample_length + 0.1) * self.device_info['sample_rate'])))
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pad_eeg_test[:, :int(self.sample_length * self.device_info['sample_rate'])] = data
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@@ -384,28 +385,33 @@ class Decoder_main(threading.Thread):
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'''训练阶段采集数据'''
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if self.zmqServer.state_mode == 'train' and self.train_started == False: # 训练状态
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if self.zmqServer.epoch_finished and self.zmqServer.paradigmBuffer.GetDataLenCount() >= \
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self.zmqServer.train_epoch[1] + self.zmqServer.event_inner_idx:
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self.currentLabel = self.zmqServer.currentLabel # 同步当前标签
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algo_log(f"训练队列数据：{self.zmqServer.paradigmBuffer.GetDataLenCount()}", level="DEBUG")
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originalTrial = self.zmqServer.paradigmBuffer.get_MIData() # 取出MI导联数据
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algo_log(f"取出的：{originalTrial.shape},event: {originalTrial[-2, self.zmqServer.event_inner_idx]}", level="DEBUG")
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try:
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epoch_payload = self.zmqServer.epoch_queue.get_nowait()
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except queue.Empty:
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time.sleep(0.0001)
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return
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originalTrial = epoch_payload['snapshot']
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event_inner_idx = epoch_payload['event_inner_idx']
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self.currentLabel = epoch_payload['currentLabel']
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data_length = epoch_payload['data_length']
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if originalTrial is None or data_length < self.zmqServer.train_epoch[1] + event_inner_idx:
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algo_log(f"epoch数据长度不足: {data_length}, 跳过", level="WARNING")
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return
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algo_log(f"训练队列数据：{data_length}", level="DEBUG")
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algo_log(f"取出的：{originalTrial.shape},event: {originalTrial[-2, event_inner_idx]}", level="DEBUG")
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trainTrial = self.preprocess(originalTrial[:self.n_chan, :]) # 预处理
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trainTrial = trainTrial[:, self.zmqServer.event_inner_idx + self.interval_epoch[
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0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]]
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# algo_log(f"trial: {self.zmqServer.event_inner_idx},{self.interval_epoch[0]},{self.interval_epoch[1]}", level="DEBUG")
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trainTrial = trainTrial[:, event_inner_idx + self.interval_epoch[
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0]:event_inner_idx + self.interval_epoch[1]]
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# algo_log(f"trial: {event_inner_idx},{self.interval_epoch[0]},{self.interval_epoch[1]}", level="DEBUG")
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if trainTrial.shape[1] == (self.interval_epoch[1] - self.interval_epoch[0]) and isinstance(self.trainLabel,
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list) \
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and self.trainLabel.count(self.currentLabel) < self.single_train:
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self.trainData.append(trainTrial)
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self.trainLabel.append(self.currentLabel)
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algo_log(f"训练集：{np.shape(self.trainData)}", level="DEBUG")
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self.plotData.append(originalTrial[:self.n_chan, self.zmqServer.event_inner_idx + self.interval_epoch[
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0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]])
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self.plotData.append(originalTrial[:self.n_chan, event_inner_idx + self.interval_epoch[
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0]:event_inner_idx + self.interval_epoch[1]])
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self.plotLabel.append(self.currentLabel)
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else:
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time.sleep(0.001)
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return
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elif self.zmqServer.state_mode == 'predict' and self.load_model == True: # 测试状态
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if self.zmqServer.StartDecode:
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@@ -414,21 +420,26 @@ class Decoder_main(threading.Thread):
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formatted_time = now.strftime('%H:%M:%S.%f')[:-3]
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algo_log(f"MI启动预测 {formatted_time}", level="DEBUG")
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if self.zmqServer.epoch_finished == False or self.zmqServer.paradigmBuffer.GetDataLenCount() < \
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self.interval_epoch[1] \
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+ self.zmqServer.event_inner_idx:
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try:
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epoch_payload = self.zmqServer.epoch_queue.get_nowait()
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except queue.Empty:
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time.sleep(0.001)
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return
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originalData = self.zmqServer.paradigmBuffer.get_MIData() # 读取全部数据
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algo_log(f"取出的：{originalData.shape},event: {originalData[-2, self.zmqServer.event_inner_idx]}", level="DEBUG")
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originalData = epoch_payload['snapshot']
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event_inner_idx = epoch_payload['event_inner_idx']
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data_length = epoch_payload['data_length']
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if originalData is None or data_length < self.interval_epoch[1] + event_inner_idx:
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algo_log(f"predict epoch数据长度不足: {data_length}, 跳过", level="WARNING")
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return
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algo_log(f"取出的：{originalData.shape},event: {originalData[-2, event_inner_idx]}", level="DEBUG")
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start = time.time()
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data = self.preprocess(originalData[:self.n_chan, :]) # 预处理
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data = data[:,
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self.zmqServer.event_inner_idx + self.interval_epoch[
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0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]]
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event_inner_idx + self.interval_epoch[
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0]:event_inner_idx + self.interval_epoch[1]]
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self.plotData.append(
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originalData[:self.n_chan, self.zmqServer.event_inner_idx + self.interval_epoch[
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0]:self.zmqServer.event_inner_idx + self.interval_epoch[1]])
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originalData[:self.n_chan, event_inner_idx + self.interval_epoch[
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0]:event_inner_idx + self.interval_epoch[1]])
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test_data = data[np.newaxis, np.newaxis, :, :]
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test_data = torch.from_numpy(test_data)
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@@ -84,6 +84,10 @@ class zmqServer(threading.Thread):
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self.cmd_send_queue = queue.Queue() # 8099端口命令结果队列
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self.data_send_queue = queue.Queue() # 8100端口滤波数据队列
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# epoch完成通知队列：生产者(zmqServer)写入, 消费者(Decoder)读取
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# 每个元素是一个dict，包含完整的epoch数据快照，避免裸标志位竞态
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self.epoch_queue = queue.Queue(maxsize=10)
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# 范式buffer与事件检测参数
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self.predict_event = 99
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self.events = [1, 2, self.predict_event]
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@@ -105,6 +109,12 @@ class zmqServer(threading.Thread):
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self.pack_contain_event = False
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self.event_inner_idx = -1
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self.interval_inited = False
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# 清空epoch队列，防止残留旧epoch被新阶段消费
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while not self.epoch_queue.empty():
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try:
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self.epoch_queue.get_nowait()
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except queue.Empty:
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break
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def interval_init(self, decoder_class):
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if decoder_class == 'ssmvep':
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@@ -132,6 +142,12 @@ class zmqServer(threading.Thread):
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self.predict_event = 99
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self.events = [1, 2, self.predict_event]
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self.interval_inited = True
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# 清空epoch队列，防止旧范式的残留epoch被新阶段消费
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while not self.epoch_queue.empty():
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try:
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self.epoch_queue.get_nowait()
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except queue.Empty:
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break
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# -------------------------- 8099端口：命令结果广播 --------------------------
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def broadcast_message(self, method, params):
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@@ -341,10 +357,60 @@ class zmqServer(threading.Thread):
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with self.paradigmBufferLock:
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self.paradigmBuffer.appendBuffer(data_np)
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if self.interval_inited:
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self.pack_contain_event, self.epoch_finished = self.detect_event(data_np)
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if self.epoch_finished:
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algo_log(f"Epoch采集完成, 当前数据长度{self.paradigmBuffer.GetDataLenCount()}", level="DEBUG")
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self.epoch_finished = self.detect_event(data_np)
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if self.pack_contain_event:
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self.paradigmBuffer.resetAllPara()
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self.paradigmBuffer.appendBuffer(data_np)
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if self.epoch_finished:
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now = datetime.datetime.now()
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time_diff_str = ""
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# 计算与上一次Epoch完成的时间差
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if self.last_epoch_finish_time is not None:
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delta_seconds = (now - self.last_epoch_finish_time).total_seconds()
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time_diff_str = f" | 与上一次间隔: {delta_seconds:.3f} s"
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log_msg = f"Epoch采集完成: {now.strftime('%H:%M:%S.%f')[:-3]}{time_diff_str}"
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algo_log(log_msg, level="DEBUG")
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self.last_epoch_finish_time = now
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# ---- 队列化通知：在锁内快照数据，原子放入epoch_queue ----
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# get_MIData / get_SSMVEPData 内部调用 getData()，返回连续数组副本
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# 快照完成后再由 Decoder 线程消费，彻底消除 TOCTOU 竞态
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data_length = self.paradigmBuffer.GetDataLenCount()
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try:
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if self.decoder_class == 'mi':
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snapshot = self.paradigmBuffer.get_MIData()
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elif self.decoder_class == 'ssmvep':
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snapshot = self.paradigmBuffer.get_SSMVEPData()
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else:
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snapshot = None
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except Exception as snap_err:
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algo_log(f"epoch快照失败: {snap_err}", level="ERROR")
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snapshot = None
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epoch_payload = {
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'snapshot': snapshot, # 已选通道的数据副本
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'currentLabel': self.currentLabel,
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'event_inner_idx': self.event_inner_idx,
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'data_length': data_length,
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'completion_time': now,
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'decoder_class': self.decoder_class,
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}
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try:
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self.epoch_queue.put_nowait(epoch_payload)
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except queue.Full:
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# 队列满时丢弃最旧的epoch，保留最新
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try:
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self.epoch_queue.get_nowait()
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except queue.Empty:
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pass
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try:
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self.epoch_queue.put_nowait(epoch_payload)
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algo_log("epoch_queue已满，已丢弃最旧epoch", level="WARNING")
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except queue.Full:
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algo_log("epoch_queue放入失败", level="ERROR")
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else:
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self.paradigmBuffer.appendBuffer(data_np)
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except Exception as e:
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algo_log(f"数据处理失败: {str(e)}", level="ERROR")
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