update float32 to float64

MI/Algorithm/conformer_2class.py

@@ -82,7 +82,7 @@ class MultiHeadAttention(nn.Module):
         values = rearrange(self.values(x), "b n (h d) -> b h n d", h=self.num_heads)
         energy = torch.einsum('bhqd, bhkd -> bhqk', queries, keys)  
         if mask is not None:
-            fill_value = torch.finfo(torch.float32).min
+            fill_value = torch.finfo(torch.float64).min
             energy.mask_fill(~mask, fill_value)
 
         scaling = self.emb_size ** (1 / 2)

MI/Algorithm/conformer_2class_cpu.py

@@ -71,7 +71,7 @@ class MultiHeadAttention(nn.Module):
         values = rearrange(self.values(x), "b n (h d) -> b h n d", h=self.num_heads)
         energy = torch.einsum('bhqd, bhkd -> bhqk', queries, keys)  
         if mask is not None:
-            fill_value = torch.finfo(torch.float32).min
+            fill_value = torch.finfo(torch.float64).min
             energy.mask_fill(~mask, fill_value)
 
         scaling = self.emb_size ** (1 / 2)

Zmq/dataBuffer.py

@@ -11,7 +11,7 @@ class ParadigmRingBuffer:
     def __init__(self, n_chan, n_points):
         self.n_chan = n_chan
         self.n_points = n_points
-        self.buffer = np.zeros((n_chan, n_points), dtype=np.float32)
+        self.buffer = np.zeros((n_chan, n_points), dtype=np.float64)
         self.currentPtr = 0
         self.readPtr = 0
         self.nUpdate = 0

Zmq/filterProcess.py

@@ -18,7 +18,7 @@ class FilterRingBuffer:
         self.n_chan = n_chan
         self.n_points = n_points
 
-        self.buffer = np.zeros((n_chan, n_points), dtype=np.float32)
+        self.buffer = np.zeros((n_chan, n_points), dtype=np.float64)
         self.current_ptr = 0  # 写入指针：指向下一个要写入的位置
         self.total_samples = 0  # 已写入总点数
         self.lock = threading.Lock()  # 线程安全锁

Zmq/zmqServer.py

@@ -174,7 +174,7 @@ class zmqServer(threading.Thread):
             return
             
         # 转置为上位机需要的[50, 通道数]格式
-        filtered_data = filtered_data.T.astype(np.float32)
+        filtered_data = filtered_data.T.astype(np.float64)
         send_buf = filtered_data.tobytes()
         algo_log(f"发送滤波数据，长度: {len(send_buf)}字节, filtered_data.shape: {filtered_data.shape}", level="DEBUG")
         self.data_send_queue.put(send_buf)
@@ -292,7 +292,7 @@ class zmqServer(threading.Thread):
                 return
 
             # 零拷贝解析 + 维度转换
-            data_np = np.frombuffer(data_bytes, dtype=np.float32)
+            data_np = np.frombuffer(data_bytes, dtype=np.float64)
             data_np = data_np.reshape(self.device_info['frame_points'], self.device_info['channel_nums'])
             data_np = data_np.T.astype(np.float64)
 

datamock.py

@@ -18,7 +18,7 @@ PKT_INTERVAL = N_SAMPLES_PER_PKT / FS
 
 def build_packet(global_sample_idx):
     """
-    生成一包 [5, 66] 的 float32 数据
+    生成一包 [5, 66] 的 float64 数据
     :param global_sample_idx: 当前包第一个采样点在全局序列中的索引 (从 0 开始)
     :return: np.ndarray shape [5, 66]
     """
@@ -31,13 +31,13 @@ def build_packet(global_sample_idx):
     eeg = np.tile(eeg, (1, 64))  # [5, 64]
 
     # Ch64: 标签值通道，初始化为 0
-    event = np.zeros((N_SAMPLES_PER_PKT, 1), dtype=np.float32)
+    event = np.zeros((N_SAMPLES_PER_PKT, 1), dtype=np.float64)
 
     # Ch65: 标签序号通道，初始化为 0
-    label_idx = np.zeros((N_SAMPLES_PER_PKT, 1), dtype=np.float32)
+    label_idx = np.zeros((N_SAMPLES_PER_PKT, 1), dtype=np.float64)
 
     # 拼成 [5, 66]
-    packet = np.concatenate([eeg, event, label_idx], axis=1).astype(np.float32)
+    packet = np.concatenate([eeg, event, label_idx], axis=1).astype(np.float64)
     return packet