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