onnx

游雁

2023-04-07 b9837bfc73c14f74cbb4c351bb51b35f1d354ac6

onnx

 funasr/export/models/__init__.py

@@ -4,10 +4,10 @@
from funasr.models.e2e_vad import E2EVadModel
from funasr.export.models.e2e_vad import E2EVadModel as E2EVadModel_export
from funasr.models.target_delay_transformer import TargetDelayTransformer
from funasr.export.models.target_delay_transformer import TargetDelayTransformer as TargetDelayTransformer_export
from funasr.export.models.target_delay_transformer import CT_Transformer as CT_Transformer_export
from funasr.train.abs_model import PunctuationModel
from funasr.models.vad_realtime_transformer import VadRealtimeTransformer
from funasr.export.models.vad_realtime_transformer import VadRealtimeTransformer as VadRealtimeTransformer_export
from funasr.export.models.target_delay_transformer import CT_Transformer_VadRealtime as CT_Transformer_VadRealtime_export

def get_model(model, export_config=None):
    if isinstance(model, BiCifParaformer):
@@ -18,8 +18,8 @@
        return E2EVadModel_export(model, **export_config)
    elif isinstance(model, PunctuationModel):
        if isinstance(model.punc_model, TargetDelayTransformer):
            return TargetDelayTransformer_export(model.punc_model, **export_config)
            return CT_Transformer_export(model.punc_model, **export_config)
        elif isinstance(model.punc_model, VadRealtimeTransformer):
            return VadRealtimeTransformer_export(model.punc_model, **export_config)
            return CT_Transformer_VadRealtime_export(model.punc_model, **export_config)
    else:
        raise "Funasr does not support the given model type currently."

 funasr/export/models/target_delay_transformer.py

@@ -3,7 +3,12 @@
import torch
import torch.nn as nn

class TargetDelayTransformer(nn.Module):
from funasr.models.encoder.sanm_encoder import SANMEncoder
from funasr.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export
from funasr.models.encoder.sanm_encoder import SANMVadEncoder
from funasr.export.models.encoder.sanm_encoder import SANMVadEncoder as SANMVadEncoder_export

class CT_Transformer(nn.Module):

    def __init__(
            self,
@@ -23,16 +28,12 @@
        self.num_embeddings = self.embed.num_embeddings
        self.model_name = model_name

        # from funasr.models.encoder.sanm_encoder import SANMEncoder as Encoder
        from funasr.models.encoder.sanm_encoder import SANMEncoder
        from funasr.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export

        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]:
    def forward(self, inputs: torch.Tensor, text_lengths: torch.Tensor) -> Tuple[torch.Tensor, None]:
        """Compute loss value from buffer sequences.

        Args:
@@ -40,7 +41,7 @@
            hidden (torch.Tensor): Target ids. (batch, len)

        """
        x = self.embed(input)
        x = self.embed(inputs)
        # mask = self._target_mask(input)
        h, _ = self.encoder(x, text_lengths)
        y = self.decoder(h)
@@ -53,14 +54,14 @@
        return (text_indexes, text_lengths)

    def get_input_names(self):
        return ['input', 'text_lengths']
        return ['inputs', 'text_lengths']

    def get_output_names(self):
        return ['logits']

    def get_dynamic_axes(self):
        return {
            'input': {
            'inputs': {
                0: 'batch_size',
                1: 'feats_length'
            },
@@ -73,3 +74,81 @@
            },
        }


class CT_Transformer_VadRealtime(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
        if isinstance(model.encoder, SANMVadEncoder):
            self.encoder = SANMVadEncoder_export(model.encoder, onnx=onnx)
        else:
            assert False, "Only support samn encode."
        self.decoder = model.decoder
        self.model_name = model_name



    def forward(self, inputs: torch.Tensor,
                text_lengths: torch.Tensor,
                vad_indexes: torch.Tensor,
                sub_masks: 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(inputs)
        # mask = self._target_mask(input)
        h, _ = self.encoder(x, text_lengths, vad_indexes, sub_masks)
        y = self.decoder(h)
        return y

    def with_vad(self):
        return True

    def get_dummy_inputs(self):
        length = 120
        text_indexes = torch.randint(0, self.embed.num_embeddings, (1, length))
        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 get_input_names(self):
        return ['inputs', 'text_lengths', 'vad_masks', 'sub_masks']

    def get_output_names(self):
        return ['logits']

    def get_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'
            },
        }

 funasr/export/models/vad_realtime_transformer.py

File was deleted

 funasr/export/test/test_onnx_punc.py

@@ -9,7 +9,7 @@
    output_name = [nd.name for nd in sess.get_outputs()]

    def _get_feed_dict(text_length):
        return {'input': np.ones((1, text_length), dtype=np.int64), 'text_lengths': np.array([text_length,], dtype=np.int32)}
        return {'inputs': np.ones((1, text_length), dtype=np.int64), 'text_lengths': np.array([text_length,], dtype=np.int32)}

    def _run(feed_dict):
        output = sess.run(output_name, input_feed=feed_dict)

 funasr/export/test/test_onnx_punc_vadrealtime.py

@@ -9,9 +9,9 @@
    output_name = [nd.name for nd in sess.get_outputs()]

    def _get_feed_dict(text_length):
        return {'input': np.ones((1, text_length), dtype=np.int64),
        return {'inputs': np.ones((1, text_length), dtype=np.int64),
                'text_lengths': np.array([text_length,], dtype=np.int32),
                'vad_mask': np.ones((1, 1, text_length, text_length), dtype=np.float32),
                'vad_masks': np.ones((1, 1, text_length, text_length), dtype=np.float32),
                'sub_masks': np.tril(np.ones((text_length, text_length), dtype=np.float32))[None, None, :, :].astype(np.float32)
                }