From 4ba1011b42e041ee1d71448eefd7ef2e7bd61bb6 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 31 三月 2023 15:31:26 +0800
Subject: [PATCH] export
---
funasr/export/models/target_delay_transformer.py | 89 +-------------------------------------------
1 files changed, 2 insertions(+), 87 deletions(-)
diff --git a/funasr/export/models/target_delay_transformer.py b/funasr/export/models/target_delay_transformer.py
index 0a2586c..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):
@@ -28,89 +18,14 @@
onnx = kwargs["onnx"]
self.embed = model.embed
self.decoder = model.decoder
- self.model = model
+ # self.model = model
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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