Upload audio/DF_Arena_1B_V_1/conformer.py with huggingface_hub

audio/DF_Arena_1B_V_1/conformer.py

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+import math
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+import torch
+import torch.nn as nn
+from torch.nn.modules.transformer import _get_clones
+from torch import Tensor
+from einops import rearrange
+from einops.layers.torch import Rearrange
+
+# helper functions
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+def calc_same_padding(kernel_size):
+    pad = kernel_size // 2
+    return (pad, pad - (kernel_size + 1) % 2)
+
+# helper classes
+
+class Swish(nn.Module):
+    def forward(self, x):
+        return x * x.sigmoid()
+
+class GLU(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x):
+        out, gate = x.chunk(2, dim=self.dim)
+        return out * gate.sigmoid()
+
+class DepthWiseConv1d(nn.Module):
+    def __init__(self, chan_in, chan_out, kernel_size, padding):
+        super().__init__()
+        self.padding = padding
+        self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, groups = chan_in)
+
+    def forward(self, x):
+        x = F.pad(x, self.padding)
+        return self.conv(x)
+
+# attention, feedforward, and conv module
+
+class Scale(nn.Module):
+    def __init__(self, scale, fn):
+        super().__init__()
+        self.fn = fn
+        self.scale = scale
+
+    def forward(self, x, **kwargs):
+        return self.fn(x, **kwargs) * self.scale
+
+class PreNorm(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.fn = fn
+        self.norm = nn.LayerNorm(dim)
+
+    def forward(self, x, **kwargs):
+        x = self.norm(x)
+        return self.fn(x, **kwargs)
+
+class Attention(nn.Module):
+    # Head Token attention: https://arxiv.org/pdf/2210.05958.pdf
+    def __init__(self, dim, heads=8, dim_head=64, qkv_bias=False, dropout=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = heads
+        inner_dim = dim_head * heads
+        self.scale = dim_head ** -0.5
+
+        self.qkv = nn.Linear(dim, inner_dim * 3, bias=qkv_bias)
+
+        self.attn_drop = nn.Dropout(dropout)
+        self.proj = nn.Linear(inner_dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        
+        self.act = nn.GELU()
+        self.ht_proj = nn.Linear(dim_head, dim,bias=True)
+        self.ht_norm = nn.LayerNorm(dim_head)
+        self.pos_embed = nn.Parameter(torch.zeros(1, self.num_heads, dim))
+    
+    def forward(self, x, mask=None):
+        B, N, C = x.shape
+
+        # head token
+        head_pos = self.pos_embed.expand(x.shape[0], -1, -1)
+        x_ = x.reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) 
+        x_ = x_.mean(dim=2)  # now the shape is [B, h, 1, d//h]
+        x_ = self.ht_proj(x_).reshape(B, -1, self.num_heads, C // self.num_heads)
+        x_ = self.act(self.ht_norm(x_)).flatten(2)
+        x_ = x_ + head_pos
+        x = torch.cat([x, x_], dim=1)
+        
+        # normal mhsa
+        qkv = self.qkv(x).reshape(B, N+self.num_heads, 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)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        # attn = self.attn_drop(attn)
+        
+        x = (attn @ v).transpose(1, 2).reshape(B, N+self.num_heads, C)
+        x = self.proj(x)
+        
+        # merge head tokens into cls token
+        cls, patch, ht = torch.split(x, [1, N-1, self.num_heads], dim=1)
+        cls = cls + torch.mean(ht, dim=1, keepdim=True) + torch.mean(patch, dim=1, keepdim=True)
+        x = torch.cat([cls, patch], dim=1)
+
+        x = self.proj_drop(x)
+
+        return x, attn
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim,
+        mult = 4,
+        dropout = 0.
+    ):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(dim, dim * mult),
+            Swish(),
+            nn.Dropout(dropout),
+            nn.Linear(dim * mult, dim),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+class ConformerConvModule(nn.Module):
+    def __init__(
+        self,
+        dim,
+        causal = False,
+        expansion_factor = 2,
+        kernel_size = 31,
+        dropout = 0.
+    ):
+        super().__init__()
+
+        inner_dim = dim * expansion_factor
+        padding = calc_same_padding(kernel_size) if not causal else (kernel_size - 1, 0)
+
+        self.net = nn.Sequential(
+            nn.LayerNorm(dim),
+            Rearrange('b n c -> b c n'),
+            nn.Conv1d(dim, inner_dim * 2, 1),
+            GLU(dim=1),
+            DepthWiseConv1d(inner_dim, inner_dim, kernel_size = kernel_size, padding = padding),
+            nn.BatchNorm1d(inner_dim) if not causal else nn.Identity(),
+            Swish(),
+            nn.Conv1d(inner_dim, dim, 1),
+            Rearrange('b c n -> b n c'),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+# Conformer Block
+
+class ConformerBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        dim_head = 64,
+        heads = 8,
+        ff_mult = 4,
+        conv_expansion_factor = 2,
+        conv_kernel_size = 31,
+        attn_dropout = 0.,
+        ff_dropout = 0.,
+        conv_dropout = 0.,
+        conv_causal = False
+    ):
+        super().__init__()
+        self.ff1 = FeedForward(dim = dim, mult = ff_mult, dropout = ff_dropout)
+        self.attn = Attention(dim = dim, dim_head = dim_head, heads = heads, dropout = attn_dropout)
+        self.conv = ConformerConvModule(dim = dim, causal = conv_causal, expansion_factor = conv_expansion_factor, kernel_size = conv_kernel_size, dropout = conv_dropout)
+        self.ff2 = FeedForward(dim = dim, mult = ff_mult, dropout = ff_dropout)
+
+        self.attn = PreNorm(dim, self.attn)
+        self.ff1 = Scale(0.5, PreNorm(dim, self.ff1))
+        self.ff2 = Scale(0.5, PreNorm(dim, self.ff2))
+
+        self.post_norm = nn.LayerNorm(dim)
+
+    def forward(self, x, mask = None):
+        x = self.ff1(x) + x
+        attn_x, attn_weight = self.attn(x, mask = mask)
+        x = attn_x + x
+        x = self.conv(x) + x
+        x = self.ff2(x) + x
+        x = self.post_norm(x)
+        return x, attn_weight
+
+# Conformer
+
+class Conformer(nn.Module):
+    def __init__(
+        self,
+        dim,
+        *,
+        depth,
+        dim_head = 64,
+        heads = 8,
+        ff_mult = 4,
+        conv_expansion_factor = 2,
+        conv_kernel_size = 31,
+        attn_dropout = 0.,
+        ff_dropout = 0.,
+        conv_dropout = 0.,
+        conv_causal = False
+    ):
+        super().__init__()
+        self.dim = dim
+        self.layers = nn.ModuleList([])
+
+        for _ in range(depth):
+            self.layers.append(ConformerBlock(
+                dim = dim,
+                dim_head = dim_head,
+                heads = heads,
+                ff_mult = ff_mult,
+                conv_expansion_factor = conv_expansion_factor,
+                conv_kernel_size = conv_kernel_size,
+                conv_causal = conv_causal
+
+            ))
+
+    def forward(self, x):
+
+        for block in self.layers:
+            x = block(x)
+
+        return x
+    
+
+
+def sinusoidal_embedding(n_channels, dim):
+    pe = torch.FloatTensor([[p / (10000 ** (2 * (i // 2) / dim)) for i in range(dim)]
+                            for p in range(n_channels)])
+    pe[:, 0::2] = torch.sin(pe[:, 0::2])
+    pe[:, 1::2] = torch.cos(pe[:, 1::2])
+    return pe.unsqueeze(0)
+
+class FinalConformer(nn.Module):
+  def __init__(self, emb_size=128, heads=4, ffmult=4, exp_fac=2, kernel_size=16, n_encoders=1):
+    super(FinalConformer, self).__init__()
+    self.dim_head=int(emb_size/heads)
+    self.dim=emb_size
+    self.heads=heads
+    self.kernel_size=kernel_size
+    self.n_encoders=n_encoders
+    self.positional_emb = nn.Parameter(sinusoidal_embedding(10000, emb_size), requires_grad=False)
+    self.encoder_blocks=_get_clones(ConformerBlock( dim = emb_size, dim_head=self.dim_head, heads= heads, 
+    ff_mult = ffmult, conv_expansion_factor = exp_fac, conv_kernel_size = kernel_size),
+    n_encoders)
+    self.class_token = nn.Parameter(torch.rand(1, emb_size))
+    self.fc5 = nn.Linear(emb_size, 2)
+
+  def forward(self, x): # x shape [bs, tiempo, frecuencia]
+    x = x + self.positional_emb[:, :x.size(1), :]
+    x = torch.stack([torch.vstack((self.class_token, x[i])) for i in range(len(x))])#[bs,1+tiempo,emb_size]
+    list_attn_weight = []
+    for layer in self.encoder_blocks:
+            x, attn_weight = layer(x) #[bs,1+tiempo,emb_size]
+            list_attn_weight.append(attn_weight)
+    embedding=x[:,0,:] #[bs, emb_size]
+    out=self.fc5(embedding) #[bs,2]
+    return out, list_attn_weight
+