From 9ba0dbd98bf69c830dfcfde8f109a400cb65e4e5 Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期五, 29 三月 2024 17:24:59 +0800
Subject: [PATCH] fix func Forward

---
 funasr/models/ct_transformer/model.py |   57 +++++++++++++++++++++++++++++++++++++++++++++++++++++++--
 1 files changed, 55 insertions(+), 2 deletions(-)

diff --git a/funasr/models/ct_transformer/model.py b/funasr/models/ct_transformer/model.py
index 1891ac7..9f680fd 100644
--- a/funasr/models/ct_transformer/model.py
+++ b/funasr/models/ct_transformer/model.py
@@ -18,7 +18,6 @@
 from funasr.models.transformer.utils.nets_utils import make_pad_mask
 from funasr.models.ct_transformer.utils import split_to_mini_sentence, split_words
 
-
 if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
     from torch.cuda.amp import autocast
 else:
@@ -359,9 +358,63 @@
                         ind_append = len_tokens - i - 1
                         for _ in range(num_append):
                             new_punc_array.insert(ind_append, 1)
-        punc_array = torch.tensor(new_punc_array)
+            punc_array = torch.tensor(new_punc_array)
         
         result_i = {"key": key[0], "text": new_mini_sentence_out, "punc_array": punc_array}
         results.append(result_i)
         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):
+        """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)
+        h, _ = self.encoder(x, text_lengths)
+        y = self.decoder(h)
+        return y
+
+    def export_dummy_inputs(self):
+        length = 120
+        text_indexes = torch.randint(0, self.embed.num_embeddings, (2, length)).type(torch.int32)
+        text_lengths = torch.tensor([length-20, length], dtype=torch.int32)
+        return (text_indexes, text_lengths)
+
+    def export_input_names(self):
+        return ['inputs', 'text_lengths']
+
+    def export_output_names(self):
+        return ['logits']
+
+    def export_dynamic_axes(self):
+        return {
+            'inputs': {
+                0: 'batch_size',
+                1: 'feats_length'
+            },
+            'text_lengths': {
+                0: 'batch_size',
+            },
+            'logits': {
+                0: 'batch_size',
+                1: 'logits_length'
+            },
+        }
+    def export_name(self):
+        return "model.onnx"
\ No newline at end of file

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