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Replace AdaptiveAvgPool2d with fixed-kernel AvgPool2d for ONNX compatibility

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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>
This commit is contained in:
Hua
2026-03-11 13:58:41 +08:00
parent f5be7671bc
commit 90d6423551
2 changed files with 11 additions and 7 deletions
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10
models/lite_crnn.py
View File

@@ -66,9 +66,11 @@ class LiteCRNN(nn.Module):
nn.MaxPool2d(2, 2), # H/2, W/2
)
# 经过 4 次高度 pool: img_h / 16 (如 40 → 2, 不够整除时用自适应)
# 用 AdaptiveAvgPool 把高度压到 1
self.adaptive_pool = nn.AdaptiveAvgPool2d((1, None)) # (B, 128, 1, W')
# 经过 4 次高度 pool 后高度 = img_h/16 (向下取整)
# 例: 40→20→10→5→2
# 用固定 kernel 的 AvgPool 把剩余高度压到 1 (ONNX 兼容)
self._pool_h = img_h // 16 # 40→2, 若有余数也向下取整
self.height_pool = nn.AvgPool2d(kernel_size=(self._pool_h, 1))
# ---- RNN 序列建模 ----
self.rnn_input_size = 128
@@ -95,7 +97,7 @@ class LiteCRNN(nn.Module):
"""
# CNN
conv = self.cnn(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) — batch_first 序列

8
models/threed_cnn.py
View File

@@ -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)