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# Ke Chen | |
# knutchen@ucsd.edu | |
# HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION | |
# Model Core | |
# 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 logging | |
import pdb | |
import math | |
import random | |
from numpy.core.fromnumeric import clip, reshape | |
import torch | |
import torch.nn as nn | |
import torch.utils.checkpoint as checkpoint | |
from torchlibrosa.stft import Spectrogram, LogmelFilterBank | |
from torchlibrosa.augmentation import SpecAugmentation | |
from itertools import repeat | |
from typing import List | |
from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_, to_2tuple | |
from utils import do_mixup, interpolate | |
# 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 | |
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., proj_drop=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=.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., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=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. 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., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., | |
drop_path=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., qkv_bias=True, qk_scale=None, | |
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, | |
norm_layer=nn.LayerNorm, | |
ape=False, patch_norm=True, | |
use_checkpoint=False, norm_before_mlp='ln', config = 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 | |
# 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) | |
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=.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) | |
# A deprecated optimization for using a hierarchical output from different blocks | |
# if self.config.htsat_hier_output: | |
# self.norm = nn.ModuleList( | |
# [self.norm_layer( | |
# min( | |
# self.embed_dim * (2 ** (len(self.depths) - 1)), | |
# self.embed_dim * (2 ** (i + 1)) | |
# ) | |
# ) for i in range(len(self.depths))] | |
# ) | |
# else: | |
self.norm = self.norm_layer(self.num_features) | |
self.avgpool = nn.AdaptiveAvgPool1d(1) | |
self.maxpool = nn.AdaptiveMaxPool1d(1) | |
# A deprecated optimization for using the max value instead of average value | |
# if self.config.htsat_use_max: | |
# self.a_avgpool = nn.AvgPool1d(kernel_size=3, stride=1, padding=1) | |
# self.a_maxpool = nn.MaxPool1d(kernel_size=3, stride=1, padding=1) | |
if self.config.enable_tscam: | |
# if self.config.htsat_hier_output: | |
# self.tscam_conv = nn.ModuleList() | |
# for i in range(len(self.depths)): | |
# zoom_ratio = 2 ** min(len(self.depths) - 1, i + 1) | |
# zoom_dim = min( | |
# self.embed_dim * (2 ** (len(self.depths) - 1)), | |
# self.embed_dim * (2 ** (i + 1)) | |
# ) | |
# SF = self.spec_size // zoom_ratio // self.patch_stride[0] // self.freq_ratio | |
# self.tscam_conv.append( | |
# nn.Conv2d( | |
# in_channels = zoom_dim, | |
# out_channels = self.num_classes, | |
# kernel_size = (SF, 3), | |
# padding = (0,1) | |
# ) | |
# ) | |
# self.head = nn.Linear(num_classes * len(self.depths), num_classes) | |
# else: | |
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) | |
else: | |
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity() | |
self.apply(self._init_weights) | |
def _init_weights(self, m): | |
if isinstance(m, nn.Linear): | |
trunc_normal_(m.weight, std=.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) | |
def no_weight_decay(self): | |
return {'absolute_pos_embed'} | |
def no_weight_decay_keywords(self): | |
return {'relative_position_bias_table'} | |
def forward_features(self, x): | |
# A deprecated optimization for using a hierarchical output from different blocks | |
# if self.config.htsat_hier_output: | |
# hier_x = [] | |
# hier_attn = [] | |
frames_num = x.shape[2] | |
x = self.patch_embed(x) | |
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) | |
# A deprecated optimization for using a hierarchical output from different blocks | |
# if self.config.htsat_hier_output: | |
# hier_x.append(x) | |
# if i == len(self.layers) - 1: | |
# hier_attn.append(attn) | |
# A deprecated optimization for using a hierarchical output from different blocks | |
# if self.config.htsat_hier_output: | |
# hxs = [] | |
# fphxs = [] | |
# for i in range(len(hier_x)): | |
# hx = hier_x[i] | |
# hx = self.norm[i](hx) | |
# B, N, C = hx.shape | |
# zoom_ratio = 2 ** min(len(self.depths) - 1, i + 1) | |
# SF = frames_num // zoom_ratio // self.patch_stride[0] | |
# ST = frames_num // zoom_ratio // self.patch_stride[1] | |
# hx = hx.permute(0,2,1).contiguous().reshape(B, C, SF, ST) | |
# B, C, F, T = hx.shape | |
# c_freq_bin = F // self.freq_ratio | |
# hx = hx.reshape(B, C, F // c_freq_bin, c_freq_bin, T) | |
# hx = hx.permute(0,1,3,2,4).contiguous().reshape(B, C, c_freq_bin, -1) | |
# hx = self.tscam_conv[i](hx) | |
# hx = torch.flatten(hx, 2) | |
# fphx = interpolate(hx.permute(0,2,1).contiguous(), self.spec_size * self.freq_ratio // hx.shape[2]) | |
# hx = self.avgpool(hx) | |
# hx = torch.flatten(hx, 1) | |
# hxs.append(hx) | |
# fphxs.append(fphx) | |
# hxs = torch.cat(hxs, dim=1) | |
# fphxs = torch.cat(fphxs, dim = 2) | |
# hxs = self.head(hxs) | |
# fphxs = self.head(fphxs) | |
# output_dict = {'framewise_output': torch.sigmoid(fphxs), | |
# 'clipwise_output': torch.sigmoid(hxs)} | |
# return output_dict | |
if self.config.enable_tscam: | |
# 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 | |
latent_output = self.avgpool(torch.flatten(x,2)) | |
latent_output = torch.flatten(latent_output, 1) | |
# display the attention map, if needed | |
if self.config.htsat_attn_heatmap: | |
# for attn | |
attn = torch.mean(attn, dim = 1) | |
attn = torch.mean(attn, dim = 1) | |
attn = attn.reshape(B, SF, ST) | |
c_freq_bin = SF // self.freq_ratio | |
attn = attn.reshape(B, SF // c_freq_bin, c_freq_bin, ST) | |
attn = attn.permute(0,2,1,3).contiguous().reshape(B, c_freq_bin, -1) | |
attn = attn.mean(dim = 1) | |
attn_max = torch.max(attn, dim = 1, keepdim = True)[0] | |
attn_min = torch.min(attn, dim = 1, keepdim = True)[0] | |
attn = ((attn * 0.15) + (attn_max * 0.85 - attn_min)) / (attn_max - attn_min) | |
attn = attn.unsqueeze(dim = 2) | |
x = self.tscam_conv(x) | |
x = torch.flatten(x, 2) # B, C, T | |
# A deprecated optimization for using the max value instead of average value | |
# if self.config.htsat_use_max: | |
# x1 = self.a_maxpool(x) | |
# x2 = self.a_avgpool(x) | |
# x = x1 + x2 | |
if self.config.htsat_attn_heatmap: | |
fpx = interpolate(torch.sigmoid(x).permute(0,2,1).contiguous() * attn, 8 * self.patch_stride[1]) | |
else: | |
fpx = interpolate(torch.sigmoid(x).permute(0,2,1).contiguous(), 8 * self.patch_stride[1]) | |
# A deprecated optimization for using the max value instead of average value | |
# if self.config.htsat_use_max: | |
# x1 = self.avgpool(x) | |
# x2 = self.maxpool(x) | |
# x = x1 + x2 | |
# else: | |
x = self.avgpool(x) | |
x = torch.flatten(x, 1) | |
if self.config.loss_type == "clip_ce": | |
output_dict = { | |
'framewise_output': fpx, # already sigmoided | |
'clipwise_output': x, | |
'latent_output': latent_output | |
} | |
else: | |
output_dict = { | |
'framewise_output': fpx, # already sigmoided | |
'clipwise_output': torch.sigmoid(x), | |
'latent_output': latent_output | |
} | |
else: | |
x = self.norm(x) # B N C | |
B, N, C = x.shape | |
fpx = x.permute(0,2,1).contiguous().reshape(B, C, frames_num // (2 ** (len(self.depths) + 1)), frames_num // (2 ** (len(self.depths) + 1)) ) | |
B, C, F, T = fpx.shape | |
c_freq_bin = F // self.freq_ratio | |
fpx = fpx.reshape(B, C, F // c_freq_bin, c_freq_bin, T) | |
fpx = fpx.permute(0,1,3,2,4).contiguous().reshape(B, C, c_freq_bin, -1) | |
fpx = torch.sum(fpx, dim = 2) | |
fpx = interpolate(fpx.permute(0,2,1).contiguous(), 8 * self.patch_stride[1]) | |
x = self.avgpool(x.transpose(1, 2)) # B C 1 | |
x = torch.flatten(x, 1) | |
if self.num_classes > 0: | |
x = self.head(x) | |
fpx = self.head(fpx) | |
output_dict = {'framewise_output': torch.sigmoid(fpx), | |
'clipwise_output': torch.sigmoid(x)} | |
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):# out_feat_keys: List[str] = None): | |
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) | |
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) | |
elif self.config.enable_repeat_mode: | |
if self.training: | |
cur_pos = random.randint(0, (self.freq_ratio - 1) * self.spec_size - 1) | |
x = self.repeat_wat2img(x, cur_pos) | |
output_dict = self.forward_features(x) | |
else: | |
output_dicts = [] | |
for cur_pos in range(0, (self.freq_ratio - 1) * self.spec_size + 1, self.spec_size): | |
tx = x.clone() | |
tx = self.repeat_wat2img(tx, cur_pos) | |
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: | |
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) | |
return output_dict |