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from einops import rearrange
from torch.cuda.amp import autocast
from functools import partial
from typing import Optional, Tuple
import torchaudio.transforms as audio_transforms
from einops.layers.torch import Rearrange
import torch
import torch.nn as nn
from .dasheng import AudioPatchEmbed, Block
# if hasattr(nn.functional, 'scaled_dot_product_attention'):
# ATTENTION_MODE = 'flash'
# else:
# ATTENTION_MODE = 'math'
# print(f'attention mode is {ATTENTION_MODE}')
class Dasheng_Encoder(nn.Module):
def __init__(self,
patch_size: Tuple[int, int] = (64, 4),
patch_stride: Tuple[int, int] = (64, 4),
embed_dim: int = 768,
depth: int = 12,
num_heads=8,
mlp_ratio=4.,
qkv_bias=True,
drop_rate=0.,
attn_drop_rate=0.,
norm_layer=None,
act_layer=None,
init_values=None,
target_length=1008,
pooling='mean',
time_patch_out: Optional[float] = None,
freq_patch_out: Optional[float] = None,
block_type='Block',
attention_type='Attention',
eval_avg='cat',
n_fft: int = 512,
n_mels: int = 64,
hop_size: int = 160,
win_size: int = 512,
f_min: int = 0,
f_max: int = 8000,
center: bool = True,
**kwargs):
super().__init__()
self.pooling = pooling
self.embed_dim = embed_dim
self.patch_stride = patch_stride
self.patch_size = patch_size
self.n_mels = n_mels
self.eval_avg = eval_avg
self.time_patch_out = time_patch_out
self.freq_patch_out = freq_patch_out
self.front_end = nn.Sequential(
audio_transforms.MelSpectrogram(f_min=f_min,
sample_rate=16000,
win_length=win_size,
center=center,
n_fft=n_fft,
f_max=f_max,
hop_length=hop_size,
n_mels=self.n_mels,
power=1))
self.to_db = audio_transforms.AmplitudeToDB(stype='magnitude', top_db=kwargs.get('top_db', 120))
self.init_bn = nn.Sequential(
Rearrange('b c f t -> b f c t'),
nn.BatchNorm2d(self.n_mels, momentum=0.01),
Rearrange('b f c t -> b c f t'))
self.target_length = target_length
self.patch_embed = AudioPatchEmbed(input_size=(self.n_mels,
target_length),
embed_dim=self.embed_dim,
patch_size=self.patch_size,
flatten=False,
patch_stride=self.patch_stride)
self.num_patches = self.patch_embed.num_patches
if pooling == 'token':
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.token_pos_embed = nn.Parameter(
torch.randn(1, embed_dim) * .02)
self.time_pos_embed = nn.Parameter(
torch.randn(1, embed_dim, 1, self.patch_embed.grid_size[1]) * .02)
self.freq_pos_embed = nn.Parameter(
torch.randn(1, embed_dim, self.patch_embed.grid_size[0], 1) * .02)
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
act_layer = act_layer or nn.GELU
self.pos_drop = nn.Dropout(p=drop_rate)
self.blocks = nn.Sequential(*[
Block(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
init_values=init_values,
drop=drop_rate,
attn_drop=attn_drop_rate,
norm_layer=norm_layer,
act_layer=act_layer,
attention_type=attention_type,
) for _ in range(depth)
])
self.norm = norm_layer(embed_dim)
self.apply(self.init_weights)
if hasattr(self, 'cls_token') and self.cls_token is not None:
nn.init.normal_(self.cls_token, std=1e-6)
# group_masking = kwargs.get('group_masking', False)
# if isinstance(group_masking, bool):
# if group_masking is True:
# self.masking_func = self.random_masking_group
# else:
# self.masking_func = self.random_masking
# elif isinstance(group_masking, int):
# self.masking_func = partial(self.random_masking_group,
# group_factor=group_masking)
# @torch.jit.ignore
# def no_weight_decay(self):
# return {
# 'time_pos_embed', 'cls_token', 'freq_pos_embed', 'token_pos_embed'
# }
def init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.LayerNorm):
nn.init.constant_(module.bias, 0)
nn.init.constant_(module.weight, 1.0)
def forward_features(self, x):
x = self.patch_embed(x)
b, c, f, t = x.shape
x = x + self.time_pos_embed[:, :, :, :t]
x = x + self.freq_pos_embed[:, :, :, :] # Just for sin pos embed
x = rearrange(x, 'b c f t -> b (f t) c')
# x, mask, ids_restore = self.random_masking(x, mask_ratio)
# x, mask, ids_restore = self.masking_func(x, mask_ratio)
if self.pooling == 'token':
cls_token = self.cls_token.expand(x.shape[0], -1, -1)
cls_token = cls_token + self.token_pos_embed[:, :]
x = torch.cat((cls_token, x), dim=1)
x = self.pos_drop(x)
for block in self.blocks:
x = block(x)
# x = self.norm(x)
return x
def load_state_dict(self, state_dict, **kwargs):
if 'time_pos_embed' in state_dict and self.time_pos_embed.shape != state_dict[
'time_pos_embed'].shape:
print("Positional Embedding shape not the same with model, resizing!")
self.change_pos_embedding(state_dict)
# Call the parent class method and capture the missing/unexpected keys
missing_keys, unexpected_keys = super().load_state_dict(state_dict, strict=False, **kwargs)
# Print missing and unexpected keys
if missing_keys:
print("Missing keys:", missing_keys)
if unexpected_keys:
print("Unexpected keys:", unexpected_keys)
def change_pos_embedding(self, state_dict):
target_time_pos_embed_length = self.time_pos_embed.shape[-1]
target_freq_pos_embed_length = self.freq_pos_embed.shape[-2]
pretrained_time_pos_embed = state_dict['time_pos_embed']
pretrained_freq_pos_embed = state_dict['freq_pos_embed']
if target_freq_pos_embed_length <= pretrained_time_pos_embed.shape[-1]:
state_dict['time_pos_embed'] = pretrained_time_pos_embed[
..., :target_time_pos_embed_length]
else:
state_dict['time_pos_embed'] = torch.nn.functional.interpolate(
pretrained_time_pos_embed,
size=(1, target_time_pos_embed_length),
align_corners=False,
mode='bilinear')
if target_freq_pos_embed_length <= pretrained_freq_pos_embed.shape[-2]:
state_dict[
'freq_pos_embed'] = pretrained_freq_pos_embed[:, :, :
target_freq_pos_embed_length, :]
else:
state_dict['freq_pos_embed'] = torch.nn.functional.interpolate(
pretrained_freq_pos_embed,
size=(target_freq_pos_embed_length, 1),
align_corners=False,
mode='bilinear')
def forward_to_spec(self, x):
# Do not use fp16 for feature extraction, that is likely to get nan
with autocast(enabled=False):
X = self.front_end(x)
# X = rearrange(X, 'b f t -> b 1 f t')
# X = self.init_bn(X)
return X
def forward(self, x):
# x = self.forward_to_spec(x)
# print(x.shape)
with autocast(enabled=False):
x = self.to_db(x)
x = rearrange(x, 'b f t -> b 1 f t')
x = self.init_bn(x)
x = self.forward_features(x)
return x |