From d0cd484fdc21c06b8bc892bb2ab1c2a25fb1da8a Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 31 三月 2023 15:05:37 +0800
Subject: [PATCH] export
---
funasr/export/models/target_delay_transformer.py | 87 -------------------------------------------
1 files changed, 1 insertions(+), 86 deletions(-)
diff --git a/funasr/export/models/target_delay_transformer.py b/funasr/export/models/target_delay_transformer.py
index fd90835..bfe3ec4 100644
--- a/funasr/export/models/target_delay_transformer.py
+++ b/funasr/export/models/target_delay_transformer.py
@@ -1,17 +1,7 @@
-from typing import Any
-from typing import List
from typing import Tuple
import torch
import torch.nn as nn
-
-from funasr.export.utils.torch_function import MakePadMask
-from funasr.export.utils.torch_function import sequence_mask
-#from funasr.models.encoder.sanm_encoder import SANMEncoder as Encoder
-from funasr.punctuation.sanm_encoder import SANMEncoder
-from funasr.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export
-from funasr.punctuation.abs_model import AbsPunctuation
-
class TargetDelayTransformer(nn.Module):
@@ -32,85 +22,10 @@
self.feats_dim = self.embed.embedding_dim
self.num_embeddings = self.embed.num_embeddings
self.model_name = model_name
- from typing import Any
- from typing import List
- from typing import Tuple
- import torch
- import torch.nn as nn
-
- from funasr.export.utils.torch_function import MakePadMask
- from funasr.export.utils.torch_function import sequence_mask
# from funasr.models.encoder.sanm_encoder import SANMEncoder as Encoder
- from funasr.punctuation.sanm_encoder import SANMEncoder
+ from funasr.models.encoder.sanm_encoder import SANMEncoder
from funasr.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export
- from funasr.punctuation.abs_model import AbsPunctuation
-
- # class TargetDelayTransformer(nn.Module):
- #
- # def __init__(
- # self,
- # model,
- # max_seq_len=512,
- # model_name='punc_model',
- # **kwargs,
- # ):
- # super().__init__()
- # onnx = False
- # if "onnx" in kwargs:
- # onnx = kwargs["onnx"]
- # self.embed = model.embed
- # self.decoder = model.decoder
- # self.model = model
- # self.feats_dim = self.embed.embedding_dim
- # self.num_embeddings = self.embed.num_embeddings
- # self.model_name = model_name
- #
- # if isinstance(model.encoder, SANMEncoder):
- # self.encoder = SANMEncoder_export(model.encoder, onnx=onnx)
- # else:
- # assert False, "Only support samn encode."
- #
- # def forward(self, input: torch.Tensor, text_lengths: torch.Tensor) -> Tuple[torch.Tensor, None]:
- # """Compute loss value from buffer sequences.
- #
- # Args:
- # input (torch.Tensor): Input ids. (batch, len)
- # hidden (torch.Tensor): Target ids. (batch, len)
- #
- # """
- # x = self.embed(input)
- # # mask = self._target_mask(input)
- # h, _ = self.encoder(x, text_lengths)
- # y = self.decoder(h)
- # return y
- #
- # def get_dummy_inputs(self):
- # length = 120
- # text_indexes = torch.randint(0, self.embed.num_embeddings, (2, length))
- # text_lengths = torch.tensor([length - 20, length], dtype=torch.int32)
- # return (text_indexes, text_lengths)
- #
- # def get_input_names(self):
- # return ['input', 'text_lengths']
- #
- # def get_output_names(self):
- # return ['logits']
- #
- # def get_dynamic_axes(self):
- # return {
- # 'input': {
- # 0: 'batch_size',
- # 1: 'feats_length'
- # },
- # 'text_lengths': {
- # 0: 'batch_size',
- # },
- # 'logits': {
- # 0: 'batch_size',
- # 1: 'logits_length'
- # },
- # }
if isinstance(model.encoder, SANMEncoder):
self.encoder = SANMEncoder_export(model.encoder, onnx=onnx)
--
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