77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
|
#!/usr/bin/env python3
|
||
|
|
# -*- encoding: utf-8 -*-
|
||
|
|
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
|
||
|
|
# MIT License (https://opensource.org/licenses/MIT)
|
||
|
|
|
||
|
|
import types
|
||
|
|
import torch
|
||
|
|
from funasr.register import tables
|
||
|
|
|
||
|
|
|
||
|
|
def export_rebuild_model(model, **kwargs):
|
||
|
|
|
||
|
|
is_onnx = kwargs.get("type", "onnx") == "onnx"
|
||
|
|
encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
|
||
|
|
model.encoder = encoder_class(model.encoder, onnx=is_onnx)
|
||
|
|
|
||
|
|
model.forward = types.MethodType(export_forward, model)
|
||
|
|
model.export_dummy_inputs = types.MethodType(export_dummy_inputs, model)
|
||
|
|
model.export_input_names = types.MethodType(export_input_names, model)
|
||
|
|
model.export_output_names = types.MethodType(export_output_names, model)
|
||
|
|
model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
|
||
|
|
model.export_name = types.MethodType(export_name, model)
|
||
|
|
|
||
|
|
return model
|
||
|
|
|
||
|
|
|
||
|
|
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"
|