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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Encoder definition."""
import torch
from espnet.nets.pytorch_backend.nets_utils import rename_state_dict
#from espnet.nets.pytorch_backend.transducer.vgg import VGG2L
from espnet.nets.pytorch_backend.transformer.attention import (
MultiHeadedAttention, # noqa: H301
RelPositionMultiHeadedAttention, # noqa: H301
LegacyRelPositionMultiHeadedAttention, # noqa: H301
)
from espnet.nets.pytorch_backend.transformer.convolution import ConvolutionModule
from espnet.nets.pytorch_backend.transformer.embedding import (
PositionalEncoding, # noqa: H301
RelPositionalEncoding, # noqa: H301
LegacyRelPositionalEncoding, # noqa: H301
)
from espnet.nets.pytorch_backend.transformer.encoder_layer import EncoderLayer
from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
from espnet.nets.pytorch_backend.transformer.multi_layer_conv import Conv1dLinear
from espnet.nets.pytorch_backend.transformer.multi_layer_conv import MultiLayeredConv1d
from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import (
PositionwiseFeedForward, # noqa: H301
)
from espnet.nets.pytorch_backend.transformer.repeat import repeat
from espnet.nets.pytorch_backend.transformer.subsampling import Conv2dSubsampling
from espnet.nets.pytorch_backend.transformer.raw_embeddings import VideoEmbedding
from espnet.nets.pytorch_backend.transformer.raw_embeddings import AudioEmbedding
from espnet.nets.pytorch_backend.backbones.conv3d_extractor import Conv3dResNet
from espnet.nets.pytorch_backend.backbones.conv1d_extractor import Conv1dResNet
def _pre_hook(
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
):
# https://github.com/espnet/espnet/commit/21d70286c354c66c0350e65dc098d2ee236faccc#diff-bffb1396f038b317b2b64dd96e6d3563
rename_state_dict(prefix + "input_layer.", prefix + "embed.", state_dict)
# https://github.com/espnet/espnet/commit/3d422f6de8d4f03673b89e1caef698745ec749ea#diff-bffb1396f038b317b2b64dd96e6d3563
rename_state_dict(prefix + "norm.", prefix + "after_norm.", state_dict)
class Encoder(torch.nn.Module):
"""Transformer encoder module.
:param int idim: input dim
:param int attention_dim: dimention of attention
:param int attention_heads: the number of heads of multi head attention
:param int linear_units: the number of units of position-wise feed forward
:param int num_blocks: the number of decoder blocks
:param float dropout_rate: dropout rate
:param float attention_dropout_rate: dropout rate in attention
:param float positional_dropout_rate: dropout rate after adding positional encoding
:param str or torch.nn.Module input_layer: input layer type
:param class pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
:param bool normalize_before: whether to use layer_norm before the first block
:param bool concat_after: whether to concat attention layer's input and output
if True, additional linear will be applied.
i.e. x -> x + linear(concat(x, att(x)))
if False, no additional linear will be applied. i.e. x -> x + att(x)
:param str positionwise_layer_type: linear of conv1d
:param int positionwise_conv_kernel_size: kernel size of positionwise conv1d layer
:param str encoder_attn_layer_type: encoder attention layer type
:param bool macaron_style: whether to use macaron style for positionwise layer
:param bool use_cnn_module: whether to use convolution module
:param bool zero_triu: whether to zero the upper triangular part of attention matrix
:param int cnn_module_kernel: kernerl size of convolution module
:param int padding_idx: padding_idx for input_layer=embed
"""
def __init__(
self,
idim,
attention_dim=256,
attention_heads=4,
linear_units=2048,
num_blocks=6,
dropout_rate=0.1,
positional_dropout_rate=0.1,
attention_dropout_rate=0.0,
input_layer="conv2d",
pos_enc_class=PositionalEncoding,
normalize_before=True,
concat_after=False,
positionwise_layer_type="linear",
positionwise_conv_kernel_size=1,
macaron_style=False,
encoder_attn_layer_type="mha",
use_cnn_module=False,
zero_triu=False,
cnn_module_kernel=31,
padding_idx=-1,
relu_type="prelu",
a_upsample_ratio=1,
):
"""Construct an Encoder object."""
super(Encoder, self).__init__()
self._register_load_state_dict_pre_hook(_pre_hook)
if encoder_attn_layer_type == "rel_mha":
pos_enc_class = RelPositionalEncoding
elif encoder_attn_layer_type == "legacy_rel_mha":
pos_enc_class = LegacyRelPositionalEncoding
# -- frontend module.
if input_layer == "conv1d":
self.frontend = Conv1dResNet(
relu_type=relu_type,
a_upsample_ratio=a_upsample_ratio,
)
elif input_layer == "conv3d":
self.frontend = Conv3dResNet(relu_type=relu_type)
else:
self.frontend = None
# -- backend module.
if input_layer == "linear":
self.embed = torch.nn.Sequential(
torch.nn.Linear(idim, attention_dim),
torch.nn.LayerNorm(attention_dim),
torch.nn.Dropout(dropout_rate),
torch.nn.ReLU(),
pos_enc_class(attention_dim, positional_dropout_rate),
)
elif input_layer == "conv2d":
self.embed = Conv2dSubsampling(
idim,
attention_dim,
dropout_rate,
pos_enc_class(attention_dim, dropout_rate),
)
elif input_layer == "vgg2l":
self.embed = VGG2L(idim, attention_dim)
elif input_layer == "embed":
self.embed = torch.nn.Sequential(
torch.nn.Embedding(idim, attention_dim, padding_idx=padding_idx),
pos_enc_class(attention_dim, positional_dropout_rate),
)
elif isinstance(input_layer, torch.nn.Module):
self.embed = torch.nn.Sequential(
input_layer, pos_enc_class(attention_dim, positional_dropout_rate),
)
elif input_layer in ["conv1d", "conv3d"]:
self.embed = torch.nn.Sequential(
torch.nn.Linear(512, attention_dim),
pos_enc_class(attention_dim, positional_dropout_rate)
)
elif input_layer is None:
self.embed = torch.nn.Sequential(
pos_enc_class(attention_dim, positional_dropout_rate)
)
else:
raise ValueError("unknown input_layer: " + input_layer)
self.normalize_before = normalize_before
if positionwise_layer_type == "linear":
positionwise_layer = PositionwiseFeedForward
positionwise_layer_args = (attention_dim, linear_units, dropout_rate)
elif positionwise_layer_type == "conv1d":
positionwise_layer = MultiLayeredConv1d
positionwise_layer_args = (
attention_dim,
linear_units,
positionwise_conv_kernel_size,
dropout_rate,
)
elif positionwise_layer_type == "conv1d-linear":
positionwise_layer = Conv1dLinear
positionwise_layer_args = (
attention_dim,
linear_units,
positionwise_conv_kernel_size,
dropout_rate,
)
else:
raise NotImplementedError("Support only linear or conv1d.")
if encoder_attn_layer_type == "mha":
encoder_attn_layer = MultiHeadedAttention
encoder_attn_layer_args = (
attention_heads,
attention_dim,
attention_dropout_rate,
)
elif encoder_attn_layer_type == "legacy_rel_mha":
encoder_attn_layer = LegacyRelPositionMultiHeadedAttention
encoder_attn_layer_args = (
attention_heads,
attention_dim,
attention_dropout_rate,
)
elif encoder_attn_layer_type == "rel_mha":
encoder_attn_layer = RelPositionMultiHeadedAttention
encoder_attn_layer_args = (
attention_heads,
attention_dim,
attention_dropout_rate,
zero_triu,
)
else:
raise ValueError("unknown encoder_attn_layer: " + encoder_attn_layer)
convolution_layer = ConvolutionModule
convolution_layer_args = (attention_dim, cnn_module_kernel)
self.encoders = repeat(
num_blocks,
lambda: EncoderLayer(
attention_dim,
encoder_attn_layer(*encoder_attn_layer_args),
positionwise_layer(*positionwise_layer_args),
convolution_layer(*convolution_layer_args) if use_cnn_module else None,
dropout_rate,
normalize_before,
concat_after,
macaron_style,
),
)
if self.normalize_before:
self.after_norm = LayerNorm(attention_dim)
def forward(self, xs, masks, extract_resnet_feats=False):
"""Encode input sequence.
:param torch.Tensor xs: input tensor
:param torch.Tensor masks: input mask
:param str extract_features: the position for feature extraction
:return: position embedded tensor and mask
:rtype Tuple[torch.Tensor, torch.Tensor]:
"""
if isinstance(self.frontend, (Conv1dResNet, Conv3dResNet)):
xs = self.frontend(xs)
if extract_resnet_feats:
return xs
if isinstance(self.embed, Conv2dSubsampling):
xs, masks = self.embed(xs, masks)
else:
xs = self.embed(xs)
xs, masks = self.encoders(xs, masks)
if isinstance(xs, tuple):
xs = xs[0]
if self.normalize_before:
xs = self.after_norm(xs)
return xs, masks
def forward_one_step(self, xs, masks, cache=None):
"""Encode input frame.
:param torch.Tensor xs: input tensor
:param torch.Tensor masks: input mask
:param List[torch.Tensor] cache: cache tensors
:return: position embedded tensor, mask and new cache
:rtype Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]:
"""
if isinstance(self.frontend, (Conv1dResNet, Conv3dResNet)):
xs = self.frontend(xs)
if isinstance(self.embed, Conv2dSubsampling):
xs, masks = self.embed(xs, masks)
else:
xs = self.embed(xs)
if cache is None:
cache = [None for _ in range(len(self.encoders))]
new_cache = []
for c, e in zip(cache, self.encoders):
xs, masks = e(xs, masks, cache=c)
new_cache.append(xs)
if self.normalize_before:
xs = self.after_norm(xs)
return xs, masks, new_cache
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