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import torch
from torch import nn
from .Patcher import PatchStrategy
from .mwmae import MWMHABlock
from .pos_embed import get_2d_sincos_pos_embed
from .utils import PatchEmbed, create_pretrained_model, repeat_token
from einops import rearrange
from typing import List
def conv3x3(in_channels, out_channels, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(
in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False
)
class GRAMT(nn.Module):
def __init__(
self,
model_size="base",
in_channels = 2,
decoder_mlp_ratio: float = 4.0,
decoder_depth: int = 8,
decoder_num_heads: int = 8,
decoder_embedding_dim: int = 512,
decoder_window_sizes: List[int] = [2, 5, 10, 25, 50, 100, 0, 0],
encoder_num_layers = 12,
encoder_num_heads = 12,
encoder_hidden_dim = 768,
encoder_mlp_ratio = 4.0,
encoder_dropout = 0.0,
encoder_attention_dropout = 0.0,
encoder_norm_layer_eps = 1e-6,
patch_size = (16,8),
frequency_stride = 16,
time_stride = 8,
input_length = 200,
num_mel_bins = 128,
**kwargs,
):
super().__init__()
self.in_channels = in_channels
self.input_length = input_length
# Calculate intermediate shape after masking
self.patch_strategy = PatchStrategy(tstride = time_stride,
tshape = patch_size[1],
fstride = frequency_stride,
fshape = patch_size[0],
input_fdim = num_mel_bins,
input_tdim = self.input_length)
self.p_f_dim, self.p_t_dim = self.patch_strategy.get_patch_size()
self.num_patches = self.p_f_dim * self.p_t_dim
self.grid_size = (self.p_f_dim, self.p_t_dim)
# This is our encoder.
# --------------------------------------------------------------------------
# Transformer
(
self.encoder,
self.encoder_embedding_dim,
) = create_pretrained_model(model_size,
encoder_num_layers = encoder_num_layers,
encoder_num_heads = encoder_num_heads,
encoder_hidden_dim = encoder_hidden_dim,
encoder_mlp_dim = int(encoder_hidden_dim * encoder_mlp_ratio),
encoder_dropout = encoder_dropout,
encoder_attention_dropout = encoder_attention_dropout,
encoder_norm_layer_eps = encoder_norm_layer_eps)
self.encoder_cls_token_num = 1
# Patch Embedder
self.patch_embed = PatchEmbed()
self._update_patch_embed_layers(self.patch_embed)
# Norm/Pos
self.register_buffer("cls_token",nn.Parameter(torch.zeros([1, 1, self.encoder_embedding_dim]), requires_grad = True))
torch.nn.init.normal_(self.cls_token, std=0.02)
# This is our decoder.
# --------------------------------------------------------------------------
# MAE decoder specifics
self.decoder_depth = decoder_depth
self.decoder_num_heads = decoder_num_heads
self.decoder_embedding_dim = decoder_embedding_dim
self.decoder_window_sizes = decoder_window_sizes
self.decoder_embed = nn.Linear(
self.encoder_embedding_dim, self.decoder_embedding_dim, bias=True
)
self.register_buffer("mask_token", nn.Parameter(torch.zeros(1, 1, self.decoder_embedding_dim, requires_grad = True)))
torch.nn.init.normal_(self.mask_token, std=0.02)
self.decoder_blocks = nn.ModuleList(
[
MWMHABlock(
dim=decoder_embedding_dim,
num_heads=decoder_num_heads,
window_sizes=decoder_window_sizes,
shift_windows=False,
mlp_ratio=decoder_mlp_ratio,
qkv_bias=True,
norm_layer=nn.LayerNorm,
)
for i in range(self.decoder_depth)
]
)
cls_token_num = 0
self.encoder.pos_embedding = self._get_pos_embed_params()
# Pos Embed init w/o the cls token num
self.register_buffer("decoder_pos_embed", nn.Parameter(
torch.zeros(1, self.num_patches, decoder_embedding_dim),
requires_grad=False,
))
pos_embed = get_2d_sincos_pos_embed(
decoder_embedding_dim, self.grid_size, cls_token_num=cls_token_num
)
self.decoder_pos_embed.data.copy_(
torch.from_numpy(pos_embed).float().unsqueeze(0)
)
# Define prediction layers for Masked Auto Encoder pretraining
self.spec_pred = nn.Sequential(
nn.Linear(
decoder_embedding_dim,
self.patch_strategy.fshape
* self.patch_strategy.tshape
* self.in_channels,
bias=True,
),
)
self.decoder_norm = nn.LayerNorm(decoder_embedding_dim)
# Normalize binaural/ambisonic spectrograms with Layer norm later.
self.spectrogram_normalize = nn.LayerNorm(
[self.in_channels, num_mel_bins, self.input_length],
elementwise_affine=False
)
self.input_shape = [num_mel_bins, self.input_length]
compile_modules = kwargs.get("compile_modules", None)
if (compile_modules is not None) and (compile_modules):
self._compile_operations()
def _compile_operations(self):
"""
Use torch.compile on the extractor, encoder and decoder blocks for faster forward
"""
try:
self.forward = torch.compile(self.get_audio_representation, mode = "reduce-overhead")
except Exception as e:
print(f"Warning: Could not compile operations: {e}")
self.use_compiled_forward = False
def _get_pos_embed_params(self):
"""Calculates the pos embedding embedding parameters and returns them."""
# Update positional embedding
pos_embed = nn.Parameter(
torch.zeros(
1,
self.num_patches + self.encoder_cls_token_num,
self.encoder_embedding_dim,
),
requires_grad=False,
)
pos_embed_data = get_2d_sincos_pos_embed(
self.encoder_embedding_dim,
self.grid_size,
cls_token_num=self.encoder_cls_token_num,
)
pos_embed.data.copy_(torch.from_numpy(pos_embed_data).float().unsqueeze(0))
return pos_embed
def _update_patch_embed_layers(self, patch_embed):
"""Updates the patch embedding embedding layers."""
# Update patch projection layer
# Use 2, as the spectrogram has 2 channels
patch_embed.proj = torch.nn.Conv2d(
self.in_channels,
self.encoder_embedding_dim,
kernel_size=(self.patch_strategy.fshape, self.patch_strategy.tshape),
stride=(self.patch_strategy.fstride, self.patch_strategy.tstride),
)
patch_embed.num_patch = self.num_patches
def pass_through_encoder(self, x, non_mask_index, B):
"""Passes the input through the Encoder Transformer network."""
# Add positional embeddings to the x.
x = x + self.encoder.pos_embedding[:, self.encoder_cls_token_num :, :]
x = x[non_mask_index, :].reshape((B, -1, x.shape[-1]))
cls_token = (
self.cls_token.expand(B, -1, -1)
+ self.encoder.pos_embedding[:, :1, :]
)
try:
dist_token = (
self.encoder.dist_token.expand(B, -1, -1)
+ self.encoder.pos_embedding[:, 1:2, :]
)
x = torch.cat((cls_token, dist_token, x), dim=1)
except Exception as e:
x = torch.cat((cls_token, x), dim=1)
x = self.encoder.dropout(x)
for block in self.encoder.layers:
x = block(x)
return self.encoder.ln(x)
def pass_through_decoder(self, encoder_output, non_mask_index, B):
encoder_output = self.decoder_embed(encoder_output)
x_ = repeat_token(
self.mask_token, (B, self.num_patches)
).type_as(encoder_output)
x_[non_mask_index, :] = encoder_output[
:, self.encoder_cls_token_num :, :
].reshape((-1, encoder_output.shape[-1]))
x_ = x_.reshape((B, -1, encoder_output.shape[-1]))
# Concatenate the CLS and Possibly Distill tokens from the encoder
# We can not do it with multi windowed attention though!
# So remove the CLS token from the decoder!
if self.use_mwmae_decoder:
x = x_
return_cut = 0
else:
x = torch.cat(
[encoder_output[:, : self.encoder_cls_token_num, :], x_], dim=1
)
return_cut = self.encoder_cls_token_num
x = x + self.decoder_pos_embed # add the pos embeds
# Pass through transformer blocks
for blk in self.decoder_blocks:
x = blk(x)
x = self.decoder_norm(x)
pred = self.spec_pred(x)
pred = pred[:, return_cut:, :]
return pred
def _get_segment_representation(self, x, strategy="mean"):
"""Extract audio representation using different strategies."""
# Put the model in eval mode when getting representations.
assert x.shape[1] == self.in_channels, f"The GRAM has in channels {self.in_channels}, but the feature has shape {x.shape} which the channels are incompatible"
B = x.shape[0]
x = x.transpose(2, 3)
x = self.spectrogram_normalize(x)
patches = self.patch_strategy.patch(x)
patches = patches.flatten(2)
encoded_patches = self.patch_strategy.embed(x, self.patch_embed)
mask = torch.zeros((B, self.num_patches), dtype=torch.bool, device=x.device)
x = self.pass_through_encoder(encoded_patches, ~mask, B)
if strategy == "mean":
return x[:, self.encoder_cls_token_num :, :].mean(axis=1)
elif strategy == "sum":
return x[:, self.encoder_cls_token_num :, :].sum(axis=1)
elif strategy == "cls":
return x[:, 0, :]
elif strategy == "raw":
x = x[:, self.encoder_cls_token_num :, :]
grid_size = self.grid_size
f, t = grid_size
# We have 25 time patches in 2 second audio. We need to have 20 for STARSS22.
outcome = rearrange(
x, "b (f t) d -> b t (f d)", f=f, d=self.encoder_embedding_dim
)
return outcome
else:
raise ValueError(f"Strategy '{strategy}' is unrecognized.")
def get_audio_representation(self, x, strategy = "mean"):
unit_frames = self.input_length
cur_frames = x.shape[2]
pad_frames = unit_frames - (cur_frames % unit_frames)
if pad_frames > 0:
# Padding with constant 0s
pad_arg = (
0,
0,
0,
pad_frames,
) # (channel, channel, height, height, width, width)
x = torch.nn.functional.pad(x, pad_arg, mode="constant")
embeddings = []
# Now get the embeddings of the model.
for i in range(x.shape[2] // unit_frames):
x_inp = x[:, :, i * unit_frames : (i + 1) * unit_frames, :]
with torch.no_grad():
embedding = self._get_segment_representation(
x_inp, strategy=strategy
)
embeddings.append(embedding)
# Stack the embeddings here if it is raw
if strategy == "raw":
x = torch.hstack(embeddings)
pad_emb_frames = int(embeddings[0].shape[1] * pad_frames / unit_frames)
if pad_emb_frames > 0:
x = x[:, :-pad_emb_frames] # remove padded tail
return x
else:
x = torch.stack(embeddings, dim=1)
return x
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