# Ke Chen
# knutchen@ucsd.edu
# HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION
# Some layers designed on the model
# below codes are based and referred from https://github.com/microsoft/Swin-Transformer
# Swin Transformer for Computer Vision: https://arxiv.org/pdf/2103.14030.pdf
import torch
import torch.nn as nn
import torch.nn.functional as F
from itertools import repeat
import collections.abc
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
import torch.utils.checkpoint as checkpoint
import random
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
from torchlibrosa.augmentation import SpecAugmentation
from itertools import repeat
from .utils import do_mixup, interpolate
from .feature_fusion import iAFF, AFF, DAF
# from PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable):
return x
return tuple(repeat(x, n))
return parse
to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = _ntuple
def drop_path(x, drop_prob: float = 0.0, training: bool = False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
'survival rate' as the argument.
"""
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (
x.ndim - 1
) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
random_tensor.floor_() # binarize
output = x.div(keep_prob) * random_tensor
return output
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x, self.drop_prob, self.training)
class PatchEmbed(nn.Module):
"""2D Image to Patch Embedding"""
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
embed_dim=768,
norm_layer=None,
flatten=True,
patch_stride=16,
enable_fusion=False,
fusion_type="None",
):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
patch_stride = to_2tuple(patch_stride)
self.img_size = img_size
self.patch_size = patch_size
self.patch_stride = patch_stride
self.grid_size = (
img_size[0] // patch_stride[0],
img_size[1] // patch_stride[1],
)
self.num_patches = self.grid_size[0] * self.grid_size[1]
self.flatten = flatten
self.in_chans = in_chans
self.embed_dim = embed_dim
self.enable_fusion = enable_fusion
self.fusion_type = fusion_type
padding = (
(patch_size[0] - patch_stride[0]) // 2,
(patch_size[1] - patch_stride[1]) // 2,
)
if (self.enable_fusion) and (self.fusion_type == "channel_map"):
self.proj = nn.Conv2d(
in_chans * 4,
embed_dim,
kernel_size=patch_size,
stride=patch_stride,
padding=padding,
)
else:
self.proj = nn.Conv2d(
in_chans,
embed_dim,
kernel_size=patch_size,
stride=patch_stride,
padding=padding,
)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
if (self.enable_fusion) and (
self.fusion_type in ["daf_2d", "aff_2d", "iaff_2d"]
):
self.mel_conv2d = nn.Conv2d(
in_chans,
embed_dim,
kernel_size=(patch_size[0], patch_size[1] * 3),
stride=(patch_stride[0], patch_stride[1] * 3),
padding=padding,
)
if self.fusion_type == "daf_2d":
self.fusion_model = DAF()
elif self.fusion_type == "aff_2d":
self.fusion_model = AFF(channels=embed_dim, type="2D")
elif self.fusion_type == "iaff_2d":
self.fusion_model = iAFF(channels=embed_dim, type="2D")
def forward(self, x, longer_idx=None):
if (self.enable_fusion) and (
self.fusion_type in ["daf_2d", "aff_2d", "iaff_2d"]
):
global_x = x[:, 0:1, :, :]
# global processing
B, C, H, W = global_x.shape
assert (
H == self.img_size[0] and W == self.img_size[1]
), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
global_x = self.proj(global_x)
TW = global_x.size(-1)
if len(longer_idx) > 0:
# local processing
local_x = x[longer_idx, 1:, :, :].contiguous()
B, C, H, W = local_x.shape
local_x = local_x.view(B * C, 1, H, W)
local_x = self.mel_conv2d(local_x)
local_x = local_x.view(
B, C, local_x.size(1), local_x.size(2), local_x.size(3)
)
local_x = local_x.permute((0, 2, 3, 1, 4)).contiguous().flatten(3)
TB, TC, TH, _ = local_x.size()
if local_x.size(-1) < TW:
local_x = torch.cat(
[
local_x,
torch.zeros(
(TB, TC, TH, TW - local_x.size(-1)),
device=global_x.device,
),
],
dim=-1,
)
else:
local_x = local_x[:, :, :, :TW]
global_x[longer_idx] = self.fusion_model(global_x[longer_idx], local_x)
x = global_x
else:
B, C, H, W = x.shape
assert (
H == self.img_size[0] and W == self.img_size[1]
), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
x = self.norm(x)
return x
class Mlp(nn.Module):
"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.0,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
def norm_cdf(x):
# Computes standard normal cumulative distribution function
return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
if (mean < a - 2 * std) or (mean > b + 2 * std):
warnings.warn(
"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
"The distribution of values may be incorrect.",
stacklevel=2,
)
with torch.no_grad():
# Values are generated by using a truncated uniform distribution and
# then using the inverse CDF for the normal distribution.
# Get upper and lower cdf values
l = norm_cdf((a - mean) / std)
u = norm_cdf((b - mean) / std)
# Uniformly fill tensor with values from [l, u], then translate to
# [2l-1, 2u-1].
tensor.uniform_(2 * l - 1, 2 * u - 1)
# Use inverse cdf transform for normal distribution to get truncated
# standard normal
tensor.erfinv_()
# Transform to proper mean, std
tensor.mul_(std * math.sqrt(2.0))
tensor.add_(mean)
# Clamp to ensure it's in the proper range
tensor.clamp_(min=a, max=b)
return tensor
def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
# type: (Tensor, float, float, float, float) -> Tensor
r"""Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \leq \text{mean} \leq b`.
Args:
tensor: an n-dimensional `torch.Tensor`
mean: the mean of the normal distribution
std: the standard deviation of the normal distribution
a: the minimum cutoff value
b: the maximum cutoff value
Examples:
>>> w = torch.empty(3, 5)
>>> nn.init.trunc_normal_(w)
"""
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
if mode == "fan_in":
denom = fan_in
elif mode == "fan_out":
denom = fan_out
elif mode == "fan_avg":
denom = (fan_in + fan_out) / 2
variance = scale / denom
if distribution == "truncated_normal":
# constant is stddev of standard normal truncated to (-2, 2)
trunc_normal_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
elif distribution == "normal":
tensor.normal_(std=math.sqrt(variance))
elif distribution == "uniform":
bound = math.sqrt(3 * variance)
tensor.uniform_(-bound, bound)
else:
raise ValueError(f"invalid distribution {distribution}")
def lecun_normal_(tensor):
variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
def window_partition(x, window_size):
"""
Args:
x: (B, H, W, C)
window_size (int): window size
Returns:
windows: (num_windows*B, window_size, window_size, C)
"""
B, H, W, C = x.shape
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
windows = (
x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
)
return windows
def window_reverse(windows, window_size, H, W):
"""
Args:
windows: (num_windows*B, window_size, window_size, C)
window_size (int): Window size
H (int): Height of image
W (int): Width of image
Returns:
x: (B, H, W, C)
"""
B = int(windows.shape[0] / (H * W / window_size / window_size))
x = windows.view(
B, H // window_size, W // window_size, window_size, window_size, -1
)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
return x
class WindowAttention(nn.Module):
r"""Window based multi-head self attention (W-MSA) module with relative position bias.
It supports both of shifted and non-shifted window.
Args:
dim (int): Number of input channels.
window_size (tuple[int]): The height and width of the window.
num_heads (int): Number of attention heads.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
"""
def __init__(
self,
dim,
window_size,
num_heads,
qkv_bias=True,
qk_scale=None,
attn_drop=0.0,
proj_drop=0.0,
):
super().__init__()
self.dim = dim
self.window_size = window_size # Wh, Ww
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim**-0.5
# define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter(
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
) # 2*Wh-1 * 2*Ww-1, nH
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(self.window_size[0])
coords_w = torch.arange(self.window_size[1])
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = (
coords_flatten[:, :, None] - coords_flatten[:, None, :]
) # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(
1, 2, 0
).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += self.window_size[1] - 1
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
self.register_buffer("relative_position_index", relative_position_index)
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
trunc_normal_(self.relative_position_bias_table, std=0.02)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x, mask=None):
"""
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
"""
B_, N, C = x.shape
qkv = (
self.qkv(x)
.reshape(B_, N, 3, self.num_heads, C // self.num_heads)
.permute(2, 0, 3, 1, 4)
)
q, k, v = (
qkv[0],
qkv[1],
qkv[2],
) # make torchscript happy (cannot use tensor as tuple)
q = q * self.scale
attn = q @ k.transpose(-2, -1)
relative_position_bias = self.relative_position_bias_table[
self.relative_position_index.view(-1)
].view(
self.window_size[0] * self.window_size[1],
self.window_size[0] * self.window_size[1],
-1,
) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(
2, 0, 1
).contiguous() # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0)
if mask is not None:
nW = mask.shape[0]
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(
1
).unsqueeze(0)
attn = attn.view(-1, self.num_heads, N, N)
attn = self.softmax(attn)
else:
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x, attn
def extra_repr(self):
return f"dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}"
# We use the model based on Swintransformer Block, therefore we can use the swin-transformer pretrained model
class SwinTransformerBlock(nn.Module):
r"""Swin Transformer Block.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resulotion.
num_heads (int): Number of attention heads.
window_size (int): Window size.
shift_size (int): Shift size for SW-MSA.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float, optional): Stochastic depth rate. Default: 0.0
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(
self,
dim,
input_resolution,
num_heads,
window_size=7,
shift_size=0,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
norm_before_mlp="ln",
):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.num_heads = num_heads
self.window_size = window_size
self.shift_size = shift_size
self.mlp_ratio = mlp_ratio
self.norm_before_mlp = norm_before_mlp
if min(self.input_resolution) <= self.window_size:
# if window size is larger than input resolution, we don't partition windows
self.shift_size = 0
self.window_size = min(self.input_resolution)
assert (
0 <= self.shift_size < self.window_size
), "shift_size must in 0-window_size"
self.norm1 = norm_layer(dim)
self.attn = WindowAttention(
dim,
window_size=to_2tuple(self.window_size),
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=drop,
)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
if self.norm_before_mlp == "ln":
self.norm2 = nn.LayerNorm(dim)
elif self.norm_before_mlp == "bn":
self.norm2 = lambda x: nn.BatchNorm1d(dim)(x.transpose(1, 2)).transpose(
1, 2
)
else:
raise NotImplementedError
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=drop,
)
if self.shift_size > 0:
# calculate attention mask for SW-MSA
H, W = self.input_resolution
img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
h_slices = (
slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None),
)
w_slices = (
slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None),
)
cnt = 0
for h in h_slices:
for w in w_slices:
img_mask[:, h, w, :] = cnt
cnt += 1
mask_windows = window_partition(
img_mask, self.window_size
) # nW, window_size, window_size, 1
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(
attn_mask != 0, float(-100.0)
).masked_fill(attn_mask == 0, float(0.0))
else:
attn_mask = None
self.register_buffer("attn_mask", attn_mask)
def forward(self, x):
# pdb.set_trace()
H, W = self.input_resolution
# print("H: ", H)
# print("W: ", W)
# pdb.set_trace()
B, L, C = x.shape
# assert L == H * W, "input feature has wrong size"
shortcut = x
x = self.norm1(x)
x = x.view(B, H, W, C)
# cyclic shift
if self.shift_size > 0:
shifted_x = torch.roll(
x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
)
else:
shifted_x = x
# partition windows
x_windows = window_partition(
shifted_x, self.window_size
) # nW*B, window_size, window_size, C
x_windows = x_windows.view(
-1, self.window_size * self.window_size, C
) # nW*B, window_size*window_size, C
# W-MSA/SW-MSA
attn_windows, attn = self.attn(
x_windows, mask=self.attn_mask
) # nW*B, window_size*window_size, C
# merge windows
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
# reverse cyclic shift
if self.shift_size > 0:
x = torch.roll(
shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)
)
else:
x = shifted_x
x = x.view(B, H * W, C)
# FFN
x = shortcut + self.drop_path(x)
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x, attn
def extra_repr(self):
return (
f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
)
class PatchMerging(nn.Module):
r"""Patch Merging Layer.
Args:
input_resolution (tuple[int]): Resolution of input feature.
dim (int): Number of input channels.
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
super().__init__()
self.input_resolution = input_resolution
self.dim = dim
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
self.norm = norm_layer(4 * dim)
def forward(self, x):
"""
x: B, H*W, C
"""
H, W = self.input_resolution
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
x = x.view(B, H, W, C)
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
x = self.norm(x)
x = self.reduction(x)
return x
def extra_repr(self):
return f"input_resolution={self.input_resolution}, dim={self.dim}"
class BasicLayer(nn.Module):
"""A basic Swin Transformer layer for one stage.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resolution.
depth (int): Number of blocks.
num_heads (int): Number of attention heads.
window_size (int): Local window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
"""
def __init__(
self,
dim,
input_resolution,
depth,
num_heads,
window_size,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
norm_layer=nn.LayerNorm,
downsample=None,
use_checkpoint=False,
norm_before_mlp="ln",
):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.depth = depth
self.use_checkpoint = use_checkpoint
# build blocks
self.blocks = nn.ModuleList(
[
SwinTransformerBlock(
dim=dim,
input_resolution=input_resolution,
num_heads=num_heads,
window_size=window_size,
shift_size=0 if (i % 2 == 0) else window_size // 2,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop,
attn_drop=attn_drop,
drop_path=drop_path[i]
if isinstance(drop_path, list)
else drop_path,
norm_layer=norm_layer,
norm_before_mlp=norm_before_mlp,
)
for i in range(depth)
]
)
# patch merging layer
if downsample is not None:
self.downsample = downsample(
input_resolution, dim=dim, norm_layer=norm_layer
)
else:
self.downsample = None
def forward(self, x):
attns = []
for blk in self.blocks:
if self.use_checkpoint:
x = checkpoint.checkpoint(blk, x)
else:
x, attn = blk(x)
if not self.training:
attns.append(attn.unsqueeze(0))
if self.downsample is not None:
x = self.downsample(x)
if not self.training:
attn = torch.cat(attns, dim=0)
attn = torch.mean(attn, dim=0)
return x, attn
def extra_repr(self):
return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
# The Core of HTSAT
class HTSAT_Swin_Transformer(nn.Module):
r"""HTSAT based on the Swin Transformer
Args:
spec_size (int | tuple(int)): Input Spectrogram size. Default 256
patch_size (int | tuple(int)): Patch size. Default: 4
path_stride (iot | tuple(int)): Patch Stride for Frequency and Time Axis. Default: 4
in_chans (int): Number of input image channels. Default: 1 (mono)
num_classes (int): Number of classes for classification head. Default: 527
embed_dim (int): Patch embedding dimension. Default: 96
depths (tuple(int)): Depth of each HTSAT-Swin Transformer layer.
num_heads (tuple(int)): Number of attention heads in different layers.
window_size (int): Window size. Default: 8
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
drop_rate (float): Dropout rate. Default: 0
attn_drop_rate (float): Attention dropout rate. Default: 0
drop_path_rate (float): Stochastic depth rate. Default: 0.1
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
patch_norm (bool): If True, add normalization after patch embedding. Default: True
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
config (module): The configuration Module from config.py
"""
def __init__(
self,
spec_size=256,
patch_size=4,
patch_stride=(4, 4),
in_chans=1,
num_classes=527,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[4, 8, 16, 32],
window_size=8,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.1,
norm_layer=nn.LayerNorm,
ape=False,
patch_norm=True,
use_checkpoint=False,
norm_before_mlp="ln",
config=None,
enable_fusion=False,
fusion_type="None",
**kwargs,
):
super(HTSAT_Swin_Transformer, self).__init__()
self.config = config
self.spec_size = spec_size
self.patch_stride = patch_stride
self.patch_size = patch_size
self.window_size = window_size
self.embed_dim = embed_dim
self.depths = depths
self.ape = ape
self.in_chans = in_chans
self.num_classes = num_classes
self.num_heads = num_heads
self.num_layers = len(self.depths)
self.num_features = int(self.embed_dim * 2 ** (self.num_layers - 1))
self.drop_rate = drop_rate
self.attn_drop_rate = attn_drop_rate
self.drop_path_rate = drop_path_rate
self.qkv_bias = qkv_bias
self.qk_scale = None
self.patch_norm = patch_norm
self.norm_layer = norm_layer if self.patch_norm else None
self.norm_before_mlp = norm_before_mlp
self.mlp_ratio = mlp_ratio
self.use_checkpoint = use_checkpoint
self.enable_fusion = enable_fusion
self.fusion_type = fusion_type
# process mel-spec ; used only once
self.freq_ratio = self.spec_size // self.config.mel_bins
window = "hann"
center = True
pad_mode = "reflect"
ref = 1.0
amin = 1e-10
top_db = None
self.interpolate_ratio = 32 # Downsampled ratio
# Spectrogram extractor
self.spectrogram_extractor = Spectrogram(
n_fft=config.window_size,
hop_length=config.hop_size,
win_length=config.window_size,
window=window,
center=center,
pad_mode=pad_mode,
freeze_parameters=True,
)
# Logmel feature extractor
self.logmel_extractor = LogmelFilterBank(
sr=config.sample_rate,
n_fft=config.window_size,
n_mels=config.mel_bins,
fmin=config.fmin,
fmax=config.fmax,
ref=ref,
amin=amin,
top_db=top_db,
freeze_parameters=True,
)
# Spec augmenter
self.spec_augmenter = SpecAugmentation(
time_drop_width=64,
time_stripes_num=2,
freq_drop_width=8,
freq_stripes_num=2,
) # 2 2
self.bn0 = nn.BatchNorm2d(self.config.mel_bins)
# split spctrogram into non-overlapping patches
self.patch_embed = PatchEmbed(
img_size=self.spec_size,
patch_size=self.patch_size,
in_chans=self.in_chans,
embed_dim=self.embed_dim,
norm_layer=self.norm_layer,
patch_stride=patch_stride,
enable_fusion=self.enable_fusion,
fusion_type=self.fusion_type,
)
num_patches = self.patch_embed.num_patches
patches_resolution = self.patch_embed.grid_size
self.patches_resolution = patches_resolution
# absolute position embedding
if self.ape:
self.absolute_pos_embed = nn.Parameter(
torch.zeros(1, num_patches, self.embed_dim)
)
trunc_normal_(self.absolute_pos_embed, std=0.02)
self.pos_drop = nn.Dropout(p=self.drop_rate)
# stochastic depth
dpr = [
x.item() for x in torch.linspace(0, self.drop_path_rate, sum(self.depths))
] # stochastic depth decay rule
# build layers
self.layers = nn.ModuleList()
for i_layer in range(self.num_layers):
layer = BasicLayer(
dim=int(self.embed_dim * 2**i_layer),
input_resolution=(
patches_resolution[0] // (2**i_layer),
patches_resolution[1] // (2**i_layer),
),
depth=self.depths[i_layer],
num_heads=self.num_heads[i_layer],
window_size=self.window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=self.qkv_bias,
qk_scale=self.qk_scale,
drop=self.drop_rate,
attn_drop=self.attn_drop_rate,
drop_path=dpr[
sum(self.depths[:i_layer]) : sum(self.depths[: i_layer + 1])
],
norm_layer=self.norm_layer,
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
use_checkpoint=use_checkpoint,
norm_before_mlp=self.norm_before_mlp,
)
self.layers.append(layer)
self.norm = self.norm_layer(self.num_features)
self.avgpool = nn.AdaptiveAvgPool1d(1)
self.maxpool = nn.AdaptiveMaxPool1d(1)
SF = (
self.spec_size
// (2 ** (len(self.depths) - 1))
// self.patch_stride[0]
// self.freq_ratio
)
self.tscam_conv = nn.Conv2d(
in_channels=self.num_features,
out_channels=self.num_classes,
kernel_size=(SF, 3),
padding=(0, 1),
)
self.head = nn.Linear(num_classes, num_classes)
if (self.enable_fusion) and (
self.fusion_type in ["daf_1d", "aff_1d", "iaff_1d"]
):
self.mel_conv1d = nn.Sequential(
nn.Conv1d(64, 64, kernel_size=5, stride=3, padding=2),
nn.BatchNorm1d(64),
)
if self.fusion_type == "daf_1d":
self.fusion_model = DAF()
elif self.fusion_type == "aff_1d":
self.fusion_model = AFF(channels=64, type="1D")
elif self.fusion_type == "iaff_1d":
self.fusion_model = iAFF(channels=64, type="1D")
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore
def no_weight_decay(self):
return {"absolute_pos_embed"}
@torch.jit.ignore
def no_weight_decay_keywords(self):
return {"relative_position_bias_table"}
def forward_features(self, x, longer_idx=None):
# A deprecated optimization for using a hierarchical output from different blocks
frames_num = x.shape[2]
x = self.patch_embed(x, longer_idx=longer_idx)
if self.ape:
x = x + self.absolute_pos_embed
x = self.pos_drop(x)
for i, layer in enumerate(self.layers):
x, attn = layer(x)
# for x
x = self.norm(x)
B, N, C = x.shape
SF = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[0]
ST = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[1]
x = x.permute(0, 2, 1).contiguous().reshape(B, C, SF, ST)
B, C, F, T = x.shape
# group 2D CNN
c_freq_bin = F // self.freq_ratio
x = x.reshape(B, C, F // c_freq_bin, c_freq_bin, T)
x = x.permute(0, 1, 3, 2, 4).contiguous().reshape(B, C, c_freq_bin, -1)
# get latent_output
fine_grained_latent_output = torch.mean(x, dim=2)
fine_grained_latent_output = interpolate(
fine_grained_latent_output.permute(0, 2, 1).contiguous(),
8 * self.patch_stride[1],
)
latent_output = self.avgpool(torch.flatten(x, 2))
latent_output = torch.flatten(latent_output, 1)
# display the attention map, if needed
x = self.tscam_conv(x)
x = torch.flatten(x, 2) # B, C, T
fpx = interpolate(
torch.sigmoid(x).permute(0, 2, 1).contiguous(), 8 * self.patch_stride[1]
)
x = self.avgpool(x)
x = torch.flatten(x, 1)
output_dict = {
"framewise_output": fpx, # already sigmoided
"clipwise_output": torch.sigmoid(x),
"fine_grained_embedding": fine_grained_latent_output,
"embedding": latent_output,
}
return output_dict
def crop_wav(self, x, crop_size, spe_pos=None):
time_steps = x.shape[2]
tx = torch.zeros(x.shape[0], x.shape[1], crop_size, x.shape[3]).to(x.device)
for i in range(len(x)):
if spe_pos is None:
crop_pos = random.randint(0, time_steps - crop_size - 1)
else:
crop_pos = spe_pos
tx[i][0] = x[i, 0, crop_pos : crop_pos + crop_size, :]
return tx
# Reshape the wavform to a img size, if you want to use the pretrained swin transformer model
def reshape_wav2img(self, x):
B, C, T, F = x.shape
target_T = int(self.spec_size * self.freq_ratio)
target_F = self.spec_size // self.freq_ratio
assert (
T <= target_T and F <= target_F
), "the wav size should less than or equal to the swin input size"
# to avoid bicubic zero error
if T < target_T:
x = nn.functional.interpolate(
x, (target_T, x.shape[3]), mode="bicubic", align_corners=True
)
if F < target_F:
x = nn.functional.interpolate(
x, (x.shape[2], target_F), mode="bicubic", align_corners=True
)
x = x.permute(0, 1, 3, 2).contiguous()
x = x.reshape(
x.shape[0],
x.shape[1],
x.shape[2],
self.freq_ratio,
x.shape[3] // self.freq_ratio,
)
# print(x.shape)
x = x.permute(0, 1, 3, 2, 4).contiguous()
x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3], x.shape[4])
return x
# Repeat the wavform to a img size, if you want to use the pretrained swin transformer model
def repeat_wat2img(self, x, cur_pos):
B, C, T, F = x.shape
target_T = int(self.spec_size * self.freq_ratio)
target_F = self.spec_size // self.freq_ratio
assert (
T <= target_T and F <= target_F
), "the wav size should less than or equal to the swin input size"
# to avoid bicubic zero error
if T < target_T:
x = nn.functional.interpolate(
x, (target_T, x.shape[3]), mode="bicubic", align_corners=True
)
if F < target_F:
x = nn.functional.interpolate(
x, (x.shape[2], target_F), mode="bicubic", align_corners=True
)
x = x.permute(0, 1, 3, 2).contiguous() # B C F T
x = x[:, :, :, cur_pos : cur_pos + self.spec_size]
x = x.repeat(repeats=(1, 1, 4, 1))
return x
def forward(
self, x: torch.Tensor, mixup_lambda=None, infer_mode=False, device=None
): # out_feat_keys: List[str] = None):
if self.enable_fusion and x["longer"].sum() == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
x["longer"][torch.randint(0, x["longer"].shape[0], (1,))] = True
if not self.enable_fusion:
x = x["waveform"].to(device=device, non_blocking=True)
x = self.spectrogram_extractor(x) # (batch_size, 1, time_steps, freq_bins)
x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
x = x.transpose(1, 3)
x = self.bn0(x)
x = x.transpose(1, 3)
if self.training:
x = self.spec_augmenter(x)
if self.training and mixup_lambda is not None:
x = do_mixup(x, mixup_lambda)
x = self.reshape_wav2img(x)
output_dict = self.forward_features(x)
else:
longer_list = x["longer"].to(device=device, non_blocking=True)
x = x["mel_fusion"].to(device=device, non_blocking=True)
x = x.transpose(1, 3)
x = self.bn0(x)
x = x.transpose(1, 3)
longer_list_idx = torch.where(longer_list)[0]
if self.fusion_type in ["daf_1d", "aff_1d", "iaff_1d"]:
new_x = x[:, 0:1, :, :].clone().contiguous()
if len(longer_list_idx) > 0:
# local processing
fusion_x_local = x[longer_list_idx, 1:, :, :].clone().contiguous()
FB, FC, FT, FF = fusion_x_local.size()
fusion_x_local = fusion_x_local.view(FB * FC, FT, FF)
fusion_x_local = torch.permute(
fusion_x_local, (0, 2, 1)
).contiguous()
fusion_x_local = self.mel_conv1d(fusion_x_local)
fusion_x_local = fusion_x_local.view(
FB, FC, FF, fusion_x_local.size(-1)
)
fusion_x_local = (
torch.permute(fusion_x_local, (0, 2, 1, 3))
.contiguous()
.flatten(2)
)
if fusion_x_local.size(-1) < FT:
fusion_x_local = torch.cat(
[
fusion_x_local,
torch.zeros(
(FB, FF, FT - fusion_x_local.size(-1)),
device=device,
),
],
dim=-1,
)
else:
fusion_x_local = fusion_x_local[:, :, :FT]
# 1D fusion
new_x = new_x.squeeze(1).permute((0, 2, 1)).contiguous()
new_x[longer_list_idx] = self.fusion_model(
new_x[longer_list_idx], fusion_x_local
)
x = new_x.permute((0, 2, 1)).contiguous()[:, None, :, :]
else:
x = new_x
elif self.fusion_type in ["daf_2d", "aff_2d", "iaff_2d", "channel_map"]:
x = x # no change
if self.training:
x = self.spec_augmenter(x)
if self.training and mixup_lambda is not None:
x = do_mixup(x, mixup_lambda)
x = self.reshape_wav2img(x)
output_dict = self.forward_features(x, longer_idx=longer_list_idx)
# if infer_mode:
# # in infer mode. we need to handle different length audio input
# frame_num = x.shape[2]
# target_T = int(self.spec_size * self.freq_ratio)
# repeat_ratio = math.floor(target_T / frame_num)
# x = x.repeat(repeats=(1,1,repeat_ratio,1))
# x = self.reshape_wav2img(x)
# output_dict = self.forward_features(x)
# else:
# if x.shape[2] > self.freq_ratio * self.spec_size:
# if self.training:
# x = self.crop_wav(x, crop_size=self.freq_ratio * self.spec_size)
# x = self.reshape_wav2img(x)
# output_dict = self.forward_features(x)
# else:
# # Change: Hard code here
# overlap_size = (x.shape[2] - 1) // 4
# output_dicts = []
# crop_size = (x.shape[2] - 1) // 2
# for cur_pos in range(0, x.shape[2] - crop_size - 1, overlap_size):
# tx = self.crop_wav(x, crop_size = crop_size, spe_pos = cur_pos)
# tx = self.reshape_wav2img(tx)
# output_dicts.append(self.forward_features(tx))
# clipwise_output = torch.zeros_like(output_dicts[0]["clipwise_output"]).float().to(x.device)
# framewise_output = torch.zeros_like(output_dicts[0]["framewise_output"]).float().to(x.device)
# for d in output_dicts:
# clipwise_output += d["clipwise_output"]
# framewise_output += d["framewise_output"]
# clipwise_output = clipwise_output / len(output_dicts)
# framewise_output = framewise_output / len(output_dicts)
# output_dict = {
# 'framewise_output': framewise_output,
# 'clipwise_output': clipwise_output
# }
# else: # this part is typically used, and most easy one
# x = self.reshape_wav2img(x)
# output_dict = self.forward_features(x)
# x = self.head(x)
# We process the data in the dataloader part, in that here we only consider the input_T < fixed_T
return output_dict
def create_htsat_model(audio_cfg, enable_fusion=False, fusion_type="None"):
try:
assert audio_cfg.model_name in [
"tiny",
"base",
"large",
], "model name for HTS-AT is wrong!"
if audio_cfg.model_name == "tiny":
model = HTSAT_Swin_Transformer(
spec_size=256,
patch_size=4,
patch_stride=(4, 4),
num_classes=audio_cfg.class_num,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[4, 8, 16, 32],
window_size=8,
config=audio_cfg,
enable_fusion=enable_fusion,
fusion_type=fusion_type,
)
elif audio_cfg.model_name == "base":
model = HTSAT_Swin_Transformer(
spec_size=256,
patch_size=4,
patch_stride=(4, 4),
num_classes=audio_cfg.class_num,
embed_dim=128,
depths=[2, 2, 12, 2],
num_heads=[4, 8, 16, 32],
window_size=8,
config=audio_cfg,
enable_fusion=enable_fusion,
fusion_type=fusion_type,
)
elif audio_cfg.model_name == "large":
model = HTSAT_Swin_Transformer(
spec_size=256,
patch_size=4,
patch_stride=(4, 4),
num_classes=audio_cfg.class_num,
embed_dim=256,
depths=[2, 2, 12, 2],
num_heads=[4, 8, 16, 32],
window_size=8,
config=audio_cfg,
enable_fusion=enable_fusion,
fusion_type=fusion_type,
)
return model
except:
raise RuntimeError(
f"Import Model for {audio_cfg.model_name} not found, or the audio cfg parameters are not enough."
)