from random import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
from models.arch_util import AttentionBlock
from models.lucidrains.x_transformers import ContinuousTransformerWrapper, Encoder
from trainer.networks import register_model
from utils.util import opt_get, checkpoint
def exists(val):
return val is not None
def masked_mean(t, mask):
t = t.masked_fill(~mask, 0.)
return t.sum(dim = 1) / mask.sum(dim = 1)
class InfoNCE(nn.Module):
"""
Calculates the InfoNCE loss for self-supervised learning.
This contrastive loss enforces the embeddings of similar (positive) samples to be close
and those of different (negative) samples to be distant.
A query embedding is compared with one positive key and with one or more negative keys.
References:
https://arxiv.org/abs/1807.03748v2
https://arxiv.org/abs/2010.05113
Args:
temperature: Logits are divided by temperature before calculating the cross entropy.
reduction: Reduction method applied to the output.
Value must be one of ['none', 'sum', 'mean'].
See torch.nn.functional.cross_entropy for more details about each option.
negative_mode: Determines how the (optional) negative_keys are handled.
Value must be one of ['paired', 'unpaired'].
If 'paired', then each query sample is paired with a number of negative keys.
Comparable to a triplet loss, but with multiple negatives per sample.
If 'unpaired', then the set of negative keys are all unrelated to any positive key.
Input shape:
query: (N, D) Tensor with query samples (e.g. embeddings of the input).
positive_key: (N, D) Tensor with positive samples (e.g. embeddings of augmented input).
negative_keys (optional): Tensor with negative samples (e.g. embeddings of other inputs)
If negative_mode = 'paired', then negative_keys is a (N, M, D) Tensor.
If negative_mode = 'unpaired', then negative_keys is a (M, D) Tensor.
If None, then the negative keys for a sample are the positive keys for the other samples.
Returns:
Value of the InfoNCE Loss.
Examples:
>>> loss = InfoNCE()
>>> batch_size, num_negative, embedding_size = 32, 48, 128
>>> query = torch.randn(batch_size, embedding_size)
>>> positive_key = torch.randn(batch_size, embedding_size)
>>> negative_keys = torch.randn(num_negative, embedding_size)
>>> output = loss(query, positive_key, negative_keys)
"""
def __init__(self, temperature=0.1, reduction='mean', negative_mode='unpaired'):
super().__init__()
self.temperature = temperature
self.reduction = reduction
self.negative_mode = negative_mode
def forward(self, query, positive_key, negative_keys=None):
return info_nce(query, positive_key, negative_keys,
temperature=self.temperature,
reduction=self.reduction,
negative_mode=self.negative_mode)
def info_nce(query, positive_key, negative_keys=None, temperature=0.1, reduction='mean', negative_mode='unpaired'):
# Check input dimensionality.
if query.dim() != 2:
raise ValueError('<query> must have 2 dimensions.')
if positive_key.dim() != 2:
raise ValueError('<positive_key> must have 2 dimensions.')
if negative_keys is not None:
if negative_mode == 'unpaired' and negative_keys.dim() != 2:
raise ValueError("<negative_keys> must have 2 dimensions if <negative_mode> == 'unpaired'.")
if negative_mode == 'paired' and negative_keys.dim() != 3:
raise ValueError("<negative_keys> must have 3 dimensions if <negative_mode> == 'paired'.")
# Check matching number of samples.
if len(query) != len(positive_key):
raise ValueError('<query> and <positive_key> must must have the same number of samples.')
if negative_keys is not None:
if negative_mode == 'paired' and len(query) != len(negative_keys):
raise ValueError("If negative_mode == 'paired', then <negative_keys> must have the same number of samples as <query>.")
# Embedding vectors should have same number of components.
if query.shape[-1] != positive_key.shape[-1]:
raise ValueError('Vectors of <query> and <positive_key> should have the same number of components.')
if negative_keys is not None:
if query.shape[-1] != negative_keys.shape[-1]:
raise ValueError('Vectors of <query> and <negative_keys> should have the same number of components.')
# Normalize to unit vectors
query, positive_key, negative_keys = normalize(query, positive_key, negative_keys)
if negative_keys is not None:
# Explicit negative keys
# Cosine between positive pairs
positive_logit = torch.sum(query * positive_key, dim=1, keepdim=True)
if negative_mode == 'unpaired':
# Cosine between all query-negative combinations
negative_logits = query @ transpose(negative_keys)
elif negative_mode == 'paired':
query = query.unsqueeze(1)
negative_logits = query @ transpose(negative_keys)
negative_logits = negative_logits.squeeze(1)
# First index in last dimension are the positive samples
logits = torch.cat([positive_logit, negative_logits], dim=1)
labels = torch.zeros(len(logits), dtype=torch.long, device=query.device)
else:
# Negative keys are implicitly off-diagonal positive keys.
# Cosine between all combinations
logits = query @ transpose(positive_key)
# Positive keys are the entries on the diagonal
labels = torch.arange(len(query), device=query.device)
return F.cross_entropy(logits / temperature, labels, reduction=reduction)
def transpose(x):
return x.transpose(-2, -1)
def normalize(*xs):
return [None if x is None else F.normalize(x, dim=-1) for x in xs]
class CollapsingTransformer(nn.Module):
def __init__(self, model_dim, output_dims, heads, dropout, depth, mask_percentage=0, **encoder_kwargs):
super().__init__()
self.transformer = ContinuousTransformerWrapper(
max_seq_len=-1,
use_pos_emb=False,
attn_layers=Encoder(
dim=model_dim,
depth=depth,
heads=heads,
ff_dropout=dropout,
ff_mult=1,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True,
**encoder_kwargs,
))
self.pre_combiner = nn.Sequential(nn.Conv1d(model_dim, output_dims, 1),
AttentionBlock(output_dims, num_heads=heads, do_checkpoint=False),
nn.Conv1d(output_dims, output_dims, 1))
self.mask_percentage = mask_percentage
def forward(self, x, **transformer_kwargs):
h = self.transformer(x, **transformer_kwargs)
h = h.permute(0,2,1)
h = checkpoint(self.pre_combiner, h).permute(0,2,1)
if self.training:
mask = torch.rand_like(h.float()) > self.mask_percentage
else:
mask = torch.ones_like(h.float()).bool()
return masked_mean(h, mask)
class ConvFormatEmbedding(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
self.emb = nn.Embedding(*args, **kwargs)
def forward(self, x):
y = self.emb(x)
return y.permute(0,2,1)
class ContrastiveAudio(nn.Module):
def __init__(
self,
model_dim=512,
transformer_heads=8,
dropout=.1,
encoder_depth=8,
mel_channels=80,
latent_multiplier=1,
mask_percent=.15,
):
super().__init__()
latent_dim = latent_multiplier*model_dim
self.temperature = nn.Parameter(torch.tensor(1.))
self.emb = nn.Sequential(nn.Conv1d(mel_channels, model_dim // 2, kernel_size=5, stride=2, padding=2),
nn.Conv1d(model_dim//2, model_dim, kernel_size=3, stride=2, padding=1))
self.transformer = CollapsingTransformer(model_dim, model_dim, transformer_heads, dropout, encoder_depth, mask_percent)
self.to_latent = nn.Linear(latent_dim, latent_dim, bias=False)
self.to_latent2 = nn.Linear(latent_dim, latent_dim, bias=False)
self.to_latent2.weight.data = self.to_latent.weight.data
self.to_latent2.weight.DO_NOT_TRAIN = True
self.to_latent2.requires_grad = False
def get_grad_norm_parameter_groups(self):
return {
'emb': list(self.emb.parameters()),
'xform': list(self.transformer.parameters()),
}
def update_for_step(self, step, __):
self.to_latent2.weight.data = self.to_latent2.weight.data * .99 + self.to_latent.weight.data * .01
def project(self, mel):
h1 = self.emb(mel).permute(0, 2, 1)
h1 = self.transformer(h1)
h1 = self.to_latent(h1)
return h1
def forward(
self,
mel_input1,
mel_input2
):
if len(mel_input2.shape) == 4:
mel_input2 = mel_input2[:, 0]
if self.training:
# Mask out big chunks of separate frequency bands for each clip.
b, c, _ = mel_input1.shape
mask = torch.rand(b,c,1, device=mel_input1.device) > .3
mel_input1 = mask * mel_input1 * (1-random()*.5)
mask = torch.rand(b,c,1, device=mel_input2.device) > .3
mel_input2 = mask * mel_input2 * (1-random()*.5)
h1 = self.emb(mel_input1).permute(0, 2, 1)
h1 = self.transformer(h1)
h1 = self.to_latent(h1)
h2 = self.emb(mel_input2).permute(0, 2, 1)
h2 = self.transformer(h2)
h2 = self.to_latent2(h2).detach()
loss = info_nce(h1, h2)
return loss
@register_model
def register_contrastive_audio(opt_net, opt):
return ContrastiveAudio(**opt_get(opt_net, ['kwargs'], {}))
if __name__ == '__main__':
clvp = ContrastiveAudio()
clvp(torch.randn(2,80,100),
torch.randn(2,80,95),
return_loss=True)
v = torch.randn(2,512)
print(info_nce(v,v))