File size: 2,381 Bytes
167f5d0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | #!/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"
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