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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)
"""Subsampling layer definition."""
import numpy as np
import torch
import torch.nn.functional as F
from funasr_detach.models.transformer.embedding import PositionalEncoding
import logging
from funasr_detach.models.scama.utils import sequence_mask
from funasr_detach.models.transformer.utils.nets_utils import (
sub_factor_to_params,
pad_to_len,
)
from typing import Optional, Tuple, Union
import math
class TooShortUttError(Exception):
"""Raised when the utt is too short for subsampling.
Args:
message (str): Message for error catch
actual_size (int): the short size that cannot pass the subsampling
limit (int): the limit size for subsampling
"""
def __init__(self, message, actual_size, limit):
"""Construct a TooShortUttError for error handler."""
super().__init__(message)
self.actual_size = actual_size
self.limit = limit
def check_short_utt(ins, size):
"""Check if the utterance is too short for subsampling."""
if isinstance(ins, Conv2dSubsampling2) and size < 3:
return True, 3
if isinstance(ins, Conv2dSubsampling) and size < 7:
return True, 7
if isinstance(ins, Conv2dSubsampling6) and size < 11:
return True, 11
if isinstance(ins, Conv2dSubsampling8) and size < 15:
return True, 15
return False, -1
class Conv2dSubsampling(torch.nn.Module):
"""Convolutional 2D subsampling (to 1/4 length).
Args:
idim (int): Input dimension.
odim (int): Output dimension.
dropout_rate (float): Dropout rate.
pos_enc (torch.nn.Module): Custom position encoding layer.
"""
def __init__(self, idim, odim, dropout_rate, pos_enc=None):
"""Construct an Conv2dSubsampling object."""
super(Conv2dSubsampling, self).__init__()
self.conv = torch.nn.Sequential(
torch.nn.Conv2d(1, odim, 3, 2),
torch.nn.ReLU(),
torch.nn.Conv2d(odim, odim, 3, 2),
torch.nn.ReLU(),
)
self.out = torch.nn.Sequential(
torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim),
pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate),
)
def forward(self, x, x_mask):
"""Subsample x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): Input mask (#batch, 1, time).
Returns:
torch.Tensor: Subsampled tensor (#batch, time', odim),
where time' = time // 4.
torch.Tensor: Subsampled mask (#batch, 1, time'),
where time' = time // 4.
"""
x = x.unsqueeze(1) # (b, c, t, f)
x = self.conv(x)
b, c, t, f = x.size()
x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
if x_mask is None:
return x, None
return x, x_mask[:, :, :-2:2][:, :, :-2:2]
def __getitem__(self, key):
"""Get item.
When reset_parameters() is called, if use_scaled_pos_enc is used,
return the positioning encoding.
"""
if key != -1:
raise NotImplementedError("Support only `-1` (for `reset_parameters`).")
return self.out[key]
class Conv2dSubsamplingPad(torch.nn.Module):
"""Convolutional 2D subsampling (to 1/4 length).
Args:
idim (int): Input dimension.
odim (int): Output dimension.
dropout_rate (float): Dropout rate.
pos_enc (torch.nn.Module): Custom position encoding layer.
"""
def __init__(self, idim, odim, dropout_rate, pos_enc=None):
"""Construct an Conv2dSubsampling object."""
super(Conv2dSubsamplingPad, self).__init__()
self.conv = torch.nn.Sequential(
torch.nn.Conv2d(1, odim, 3, 2, padding=(0, 0)),
torch.nn.ReLU(),
torch.nn.Conv2d(odim, odim, 3, 2, padding=(0, 0)),
torch.nn.ReLU(),
)
self.out = torch.nn.Sequential(
torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim),
pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate),
)
self.pad_fn = torch.nn.ConstantPad1d((0, 4), 0.0)
def forward(self, x, x_mask):
"""Subsample x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): Input mask (#batch, 1, time).
Returns:
torch.Tensor: Subsampled tensor (#batch, time', odim),
where time' = time // 4.
torch.Tensor: Subsampled mask (#batch, 1, time'),
where time' = time // 4.
"""
x = x.transpose(1, 2)
x = self.pad_fn(x)
x = x.transpose(1, 2)
x = x.unsqueeze(1) # (b, c, t, f)
x = self.conv(x)
b, c, t, f = x.size()
x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
if x_mask is None:
return x, None
x_len = torch.sum(x_mask[:, 0, :], dim=-1)
x_len = (x_len - 1) // 2 + 1
x_len = (x_len - 1) // 2 + 1
mask = sequence_mask(x_len, None, x_len.dtype, x[0].device)
return x, mask[:, None, :]
def __getitem__(self, key):
"""Get item.
When reset_parameters() is called, if use_scaled_pos_enc is used,
return the positioning encoding.
"""
if key != -1:
raise NotImplementedError("Support only `-1` (for `reset_parameters`).")
return self.out[key]
class Conv2dSubsampling2(torch.nn.Module):
"""Convolutional 2D subsampling (to 1/2 length).
Args:
idim (int): Input dimension.
odim (int): Output dimension.
dropout_rate (float): Dropout rate.
pos_enc (torch.nn.Module): Custom position encoding layer.
"""
def __init__(self, idim, odim, dropout_rate, pos_enc=None):
"""Construct an Conv2dSubsampling2 object."""
super(Conv2dSubsampling2, self).__init__()
self.conv = torch.nn.Sequential(
torch.nn.Conv2d(1, odim, 3, 2),
torch.nn.ReLU(),
torch.nn.Conv2d(odim, odim, 3, 1),
torch.nn.ReLU(),
)
self.out = torch.nn.Sequential(
torch.nn.Linear(odim * (((idim - 1) // 2 - 2)), odim),
pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate),
)
def forward(self, x, x_mask):
"""Subsample x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): Input mask (#batch, 1, time).
Returns:
torch.Tensor: Subsampled tensor (#batch, time', odim),
where time' = time // 2.
torch.Tensor: Subsampled mask (#batch, 1, time'),
where time' = time // 2.
"""
x = x.unsqueeze(1) # (b, c, t, f)
x = self.conv(x)
b, c, t, f = x.size()
x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
if x_mask is None:
return x, None
return x, x_mask[:, :, :-2:2][:, :, :-2:1]
def __getitem__(self, key):
"""Get item.
When reset_parameters() is called, if use_scaled_pos_enc is used,
return the positioning encoding.
"""
if key != -1:
raise NotImplementedError("Support only `-1` (for `reset_parameters`).")
return self.out[key]
class Conv2dSubsampling6(torch.nn.Module):
"""Convolutional 2D subsampling (to 1/6 length).
Args:
idim (int): Input dimension.
odim (int): Output dimension.
dropout_rate (float): Dropout rate.
pos_enc (torch.nn.Module): Custom position encoding layer.
"""
def __init__(self, idim, odim, dropout_rate, pos_enc=None):
"""Construct an Conv2dSubsampling6 object."""
super(Conv2dSubsampling6, self).__init__()
self.conv = torch.nn.Sequential(
torch.nn.Conv2d(1, odim, 3, 2),
torch.nn.ReLU(),
torch.nn.Conv2d(odim, odim, 5, 3),
torch.nn.ReLU(),
)
self.out = torch.nn.Sequential(
torch.nn.Linear(odim * (((idim - 1) // 2 - 2) // 3), odim),
pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate),
)
def forward(self, x, x_mask):
"""Subsample x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): Input mask (#batch, 1, time).
Returns:
torch.Tensor: Subsampled tensor (#batch, time', odim),
where time' = time // 6.
torch.Tensor: Subsampled mask (#batch, 1, time'),
where time' = time // 6.
"""
x = x.unsqueeze(1) # (b, c, t, f)
x = self.conv(x)
b, c, t, f = x.size()
x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
if x_mask is None:
return x, None
return x, x_mask[:, :, :-2:2][:, :, :-4:3]
class Conv2dSubsampling8(torch.nn.Module):
"""Convolutional 2D subsampling (to 1/8 length).
Args:
idim (int): Input dimension.
odim (int): Output dimension.
dropout_rate (float): Dropout rate.
pos_enc (torch.nn.Module): Custom position encoding layer.
"""
def __init__(self, idim, odim, dropout_rate, pos_enc=None):
"""Construct an Conv2dSubsampling8 object."""
super(Conv2dSubsampling8, self).__init__()
self.conv = torch.nn.Sequential(
torch.nn.Conv2d(1, odim, 3, 2),
torch.nn.ReLU(),
torch.nn.Conv2d(odim, odim, 3, 2),
torch.nn.ReLU(),
torch.nn.Conv2d(odim, odim, 3, 2),
torch.nn.ReLU(),
)
self.out = torch.nn.Sequential(
torch.nn.Linear(odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2), odim),
pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate),
)
def forward(self, x, x_mask):
"""Subsample x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): Input mask (#batch, 1, time).
Returns:
torch.Tensor: Subsampled tensor (#batch, time', odim),
where time' = time // 8.
torch.Tensor: Subsampled mask (#batch, 1, time'),
where time' = time // 8.
"""
x = x.unsqueeze(1) # (b, c, t, f)
x = self.conv(x)
b, c, t, f = x.size()
x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
if x_mask is None:
return x, None
return x, x_mask[:, :, :-2:2][:, :, :-2:2][:, :, :-2:2]
class Conv1dSubsampling(torch.nn.Module):
"""Convolutional 1D subsampling (to 1/2 length).
Args:
idim (int): Input dimension.
odim (int): Output dimension.
dropout_rate (float): Dropout rate.
pos_enc (torch.nn.Module): Custom position encoding layer.
"""
def __init__(
self,
idim,
odim,
kernel_size,
stride,
pad,
tf2torch_tensor_name_prefix_torch: str = "stride_conv",
tf2torch_tensor_name_prefix_tf: str = "seq2seq/proj_encoder/downsampling",
):
super(Conv1dSubsampling, self).__init__()
self.conv = torch.nn.Conv1d(idim, odim, kernel_size, stride)
self.pad_fn = torch.nn.ConstantPad1d(pad, 0.0)
self.stride = stride
self.odim = odim
self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
def output_size(self) -> int:
return self.odim
def forward(self, x, x_len):
"""Subsample x."""
x = x.transpose(1, 2) # (b, d ,t)
x = self.pad_fn(x)
# x = F.relu(self.conv(x))
x = F.leaky_relu(self.conv(x), negative_slope=0.0)
x = x.transpose(1, 2) # (b, t ,d)
if x_len is None:
return x, None
x_len = (x_len - 1) // self.stride + 1
return x, x_len
def gen_tf2torch_map_dict(self):
tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
map_dict_local = {
## predictor
"{}.conv.weight".format(tensor_name_prefix_torch): {
"name": "{}/conv1d/kernel".format(tensor_name_prefix_tf),
"squeeze": None,
"transpose": (2, 1, 0),
}, # (256,256,3),(3,256,256)
"{}.conv.bias".format(tensor_name_prefix_torch): {
"name": "{}/conv1d/bias".format(tensor_name_prefix_tf),
"squeeze": None,
"transpose": None,
}, # (256,),(256,)
}
return map_dict_local
def convert_tf2torch(
self,
var_dict_tf,
var_dict_torch,
):
map_dict = self.gen_tf2torch_map_dict()
var_dict_torch_update = dict()
for name in sorted(var_dict_torch.keys(), reverse=False):
names = name.split(".")
if names[0] == self.tf2torch_tensor_name_prefix_torch:
name_tf = map_dict[name]["name"]
data_tf = var_dict_tf[name_tf]
if map_dict[name]["squeeze"] is not None:
data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
if map_dict[name]["transpose"] is not None:
data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
var_dict_torch_update[name] = data_tf
logging.info(
"torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape
)
)
return var_dict_torch_update
class StreamingConvInput(torch.nn.Module):
"""Streaming ConvInput module definition.
Args:
input_size: Input size.
conv_size: Convolution size.
subsampling_factor: Subsampling factor.
vgg_like: Whether to use a VGG-like network.
output_size: Block output dimension.
"""
def __init__(
self,
input_size: int,
conv_size: Union[int, Tuple],
subsampling_factor: int = 4,
vgg_like: bool = True,
conv_kernel_size: int = 3,
output_size: Optional[int] = None,
) -> None:
"""Construct a ConvInput object."""
super().__init__()
if vgg_like:
if subsampling_factor == 1:
conv_size1, conv_size2 = conv_size
self.conv = torch.nn.Sequential(
torch.nn.Conv2d(
1,
conv_size1,
conv_kernel_size,
stride=1,
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size1,
conv_size1,
conv_kernel_size,
stride=1,
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.MaxPool2d((1, 2)),
torch.nn.Conv2d(
conv_size1,
conv_size2,
conv_kernel_size,
stride=1,
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size2,
conv_size2,
conv_kernel_size,
stride=1,
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.MaxPool2d((1, 2)),
)
output_proj = conv_size2 * ((input_size // 2) // 2)
self.subsampling_factor = 1
self.stride_1 = 1
self.create_new_mask = self.create_new_vgg_mask
else:
conv_size1, conv_size2 = conv_size
kernel_1 = int(subsampling_factor / 2)
self.conv = torch.nn.Sequential(
torch.nn.Conv2d(
1,
conv_size1,
conv_kernel_size,
stride=1,
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size1,
conv_size1,
conv_kernel_size,
stride=1,
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.MaxPool2d((kernel_1, 2)),
torch.nn.Conv2d(
conv_size1,
conv_size2,
conv_kernel_size,
stride=1,
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size2,
conv_size2,
conv_kernel_size,
stride=1,
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.MaxPool2d((2, 2)),
)
output_proj = conv_size2 * ((input_size // 2) // 2)
self.subsampling_factor = subsampling_factor
self.create_new_mask = self.create_new_vgg_mask
self.stride_1 = kernel_1
else:
if subsampling_factor == 1:
self.conv = torch.nn.Sequential(
torch.nn.Conv2d(1, conv_size, 3, [1, 2], [1, 0]),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size, conv_size, conv_kernel_size, [1, 2], [1, 0]
),
torch.nn.ReLU(),
)
output_proj = conv_size * (((input_size - 1) // 2 - 1) // 2)
self.subsampling_factor = subsampling_factor
self.kernel_2 = conv_kernel_size
self.stride_2 = 1
self.create_new_mask = self.create_new_conv2d_mask
else:
kernel_2, stride_2, conv_2_output_size = sub_factor_to_params(
subsampling_factor,
input_size,
)
self.conv = torch.nn.Sequential(
torch.nn.Conv2d(1, conv_size, 3, 2, [1, 0]),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size,
conv_size,
kernel_2,
stride_2,
[(kernel_2 - 1) // 2, 0],
),
torch.nn.ReLU(),
)
output_proj = conv_size * conv_2_output_size
self.subsampling_factor = subsampling_factor
self.kernel_2 = kernel_2
self.stride_2 = stride_2
self.create_new_mask = self.create_new_conv2d_mask
self.vgg_like = vgg_like
self.min_frame_length = 7
if output_size is not None:
self.output = torch.nn.Linear(output_proj, output_size)
self.output_size = output_size
else:
self.output = None
self.output_size = output_proj
def forward(
self,
x: torch.Tensor,
mask: Optional[torch.Tensor],
chunk_size: Optional[torch.Tensor],
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Encode input sequences.
Args:
x: ConvInput input sequences. (B, T, D_feats)
mask: Mask of input sequences. (B, 1, T)
Returns:
x: ConvInput output sequences. (B, sub(T), D_out)
mask: Mask of output sequences. (B, 1, sub(T))
"""
if mask is not None:
mask = self.create_new_mask(mask)
olens = max(mask.eq(0).sum(1))
b, t, f = x.size()
x = x.unsqueeze(1) # (b. 1. t. f)
if chunk_size is not None:
max_input_length = int(
chunk_size
* self.subsampling_factor
* (math.ceil(float(t) / (chunk_size * self.subsampling_factor)))
)
x = map(lambda inputs: pad_to_len(inputs, max_input_length, 1), x)
x = list(x)
x = torch.stack(x, dim=0)
N_chunks = max_input_length // (chunk_size * self.subsampling_factor)
x = x.view(b * N_chunks, 1, chunk_size * self.subsampling_factor, f)
x = self.conv(x)
_, c, _, f = x.size()
if chunk_size is not None:
x = x.transpose(1, 2).contiguous().view(b, -1, c * f)[:, :olens, :]
else:
x = x.transpose(1, 2).contiguous().view(b, -1, c * f)
if self.output is not None:
x = self.output(x)
return x, mask[:, :olens][:, : x.size(1)]
def create_new_vgg_mask(self, mask: torch.Tensor) -> torch.Tensor:
"""Create a new mask for VGG output sequences.
Args:
mask: Mask of input sequences. (B, T)
Returns:
mask: Mask of output sequences. (B, sub(T))
"""
if self.subsampling_factor > 1:
vgg1_t_len = mask.size(1) - (mask.size(1) % (self.subsampling_factor // 2))
mask = mask[:, :vgg1_t_len][:, :: self.subsampling_factor // 2]
vgg2_t_len = mask.size(1) - (mask.size(1) % 2)
mask = mask[:, :vgg2_t_len][:, ::2]
else:
mask = mask
return mask
def create_new_conv2d_mask(self, mask: torch.Tensor) -> torch.Tensor:
"""Create new conformer mask for Conv2d output sequences.
Args:
mask: Mask of input sequences. (B, T)
Returns:
mask: Mask of output sequences. (B, sub(T))
"""
if self.subsampling_factor > 1:
return mask[:, ::2][:, :: self.stride_2]
else:
return mask
def get_size_before_subsampling(self, size: int) -> int:
"""Return the original size before subsampling for a given size.
Args:
size: Number of frames after subsampling.
Returns:
: Number of frames before subsampling.
"""
return size * self.subsampling_factor
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