From 9d48230c4f8f25bf88c5d6105f97370a36c9cf43 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 11 三月 2024 10:48:50 +0800
Subject: [PATCH] export onnx (#1457)
---
funasr/models/ct_transformer_streaming/model.py | 65 ++++++++++++++++++++++++++++++++
1 files changed, 65 insertions(+), 0 deletions(-)
diff --git a/funasr/models/ct_transformer_streaming/model.py b/funasr/models/ct_transformer_streaming/model.py
index 217767a..a9b2efb 100644
--- a/funasr/models/ct_transformer_streaming/model.py
+++ b/funasr/models/ct_transformer_streaming/model.py
@@ -173,3 +173,68 @@
return results, meta_data
+ def export(
+ self,
+ **kwargs,
+ ):
+
+ is_onnx = kwargs.get("type", "onnx") == "onnx"
+ encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
+ self.encoder = encoder_class(self.encoder, onnx=is_onnx)
+
+ self.forward = self._export_forward
+
+ return self
+
+ def _export_forward(self, inputs: torch.Tensor,
+ text_lengths: torch.Tensor,
+ vad_indexes: torch.Tensor,
+ sub_masks: torch.Tensor,
+ ):
+ """Compute loss value from buffer sequences.
+
+ Args:
+ input (torch.Tensor): Input ids. (batch, len)
+ hidden (torch.Tensor): Target ids. (batch, len)
+
+ """
+ x = self.embed(inputs)
+ # mask = self._target_mask(input)
+ h, _ = self.encoder(x, text_lengths, vad_indexes, sub_masks)
+ y = self.decoder(h)
+ return y
+
+ def export_dummy_inputs(self):
+ length = 120
+ text_indexes = torch.randint(0, self.embed.num_embeddings, (1, length)).type(torch.int32)
+ text_lengths = torch.tensor([length], dtype=torch.int32)
+ vad_mask = torch.ones(length, length, dtype=torch.float32)[None, None, :, :]
+ sub_masks = torch.ones(length, length, dtype=torch.float32)
+ sub_masks = torch.tril(sub_masks).type(torch.float32)
+ return (text_indexes, text_lengths, vad_mask, sub_masks[None, None, :, :])
+
+ def export_input_names(self):
+ return ['inputs', 'text_lengths', 'vad_masks', 'sub_masks']
+
+ def export_output_names(self):
+ return ['logits']
+
+ def export_dynamic_axes(self):
+ return {
+ 'inputs': {
+ 1: 'feats_length'
+ },
+ 'vad_masks': {
+ 2: 'feats_length1',
+ 3: 'feats_length2'
+ },
+ 'sub_masks': {
+ 2: 'feats_length1',
+ 3: 'feats_length2'
+ },
+ 'logits': {
+ 1: 'logits_length'
+ },
+ }
+ def export_name(self):
+ return "model.onnx"
--
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