# ruff: noqa # vendored code â see vendored_clap/README.md # 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 math import random 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 .pytorch_utils import do_mixup, interpolate from . import config import collections.abc import warnings from torch.nn.init import _calculate_fan_in_and_fan_out 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, ): 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 padding = ((patch_size[0] - patch_stride[0]) // 2, (patch_size[1] - patch_stride[1]) // 2) 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() def forward(self, x): 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") # 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=0.0, 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, **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=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) if self.config.enable_tscam: 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=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): 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) 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 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] ) 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 = 344 # (x.shape[2] - 1) // 4 output_dicts = [] crop_size = 689 # (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) ) latent_output = ( torch.zeros_like(output_dicts[0]["latent_output"]).float().to(x.device) ) for d in output_dicts: clipwise_output += d["clipwise_output"] framewise_output += d["framewise_output"] latent_output += d["latent_output"] clipwise_output = clipwise_output / len(output_dicts) framewise_output = framewise_output / len(output_dicts) latent_output = latent_output / len(output_dicts) output_dict = { "framewise_output": framewise_output, "clipwise_output": clipwise_output, "latent_output": latent_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 class HTSATWrapper(nn.Module): def __init__( self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num, out_emb ): super().__init__() # print("parameters are being overidden when using HTSAT") # print("HTSAT only support loading a pretrained model on AudioSet") # @TODO later look at what parameters are same and can be merged self.htsat = HTSAT_Swin_Transformer(config=config) def forward(self, x): out_dict = self.htsat(x) out_dict["embedding"] = out_dict["latent_output"] return out_dict

music-assistant-server

music-assistant-server