Replace AdaptiveAvgPool2d with fixed-kernel AvgPool2d for ONNX compatibility

AdaptiveAvgPool2d with None dimensions can cause issues with some ONNX
runtimes. Use AvgPool2d with kernel=(img_h//16, 1) to achieve the same
height-to-1 reduction with full ONNX compatibility.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

models/threed_cnn.py

@@ -88,8 +88,10 @@ class ThreeDCNN(nn.Module):
             nn.MaxPool2d(kernel_size=(2, 1)),
         )
 
-        # 高度方向自适应压到 1，宽度保持
-        self.adaptive_pool = nn.AdaptiveAvgPool2d((1, None))
+        # 高度方向压到 1，宽度保持 (用固定 kernel 替代 Adaptive，ONNX 兼容)
+        # 60→30→15→7→3 (4次高度pool后)
+        self._pool_h = img_h // 16  # 60//16=3
+        self.height_pool = nn.AvgPool2d(kernel_size=(self._pool_h, 1))
 
         # ---- RNN 序列建模 ----
         self.rnn_input_size = 128
@@ -115,7 +117,7 @@ class ThreeDCNN(nn.Module):
             logits: (seq_len, batch, num_classes)  CTC 格式 (T, B, C)
         """
         conv = self.backbone(x)               # (B, 128, H', W')
-        conv = self.adaptive_pool(conv)        # (B, 128, 1, W')
+        conv = self.height_pool(conv)             # (B, 128, 1, W')
         conv = conv.squeeze(2)                 # (B, 128, W')
         conv = conv.permute(0, 2, 1)           # (B, W', 128)