import numpy as np import torch import torch.nn as nn import torch.nn.functional as F class QuantizeEMAReset(nn.Module): def __init__(self, nb_code, code_dim, mu): super().__init__() self.nb_code = nb_code self.code_dim = code_dim self.mu = mu self.reset_codebook() def reset_codebook(self): self.init = False self.code_sum = None self.code_count = None device = "cuda" if torch.cuda.is_available() else "cpu" self.register_buffer('codebook', torch.zeros(self.nb_code, self.code_dim).to(device)) def _tile(self, x): nb_code_x, code_dim = x.shape if nb_code_x < self.nb_code: n_repeats = (self.nb_code + nb_code_x - 1) // nb_code_x std = 0.01 / np.sqrt(code_dim) out = x.repeat(n_repeats, 1) out = out + torch.randn_like(out) * std else : out = x return out def init_codebook(self, x): out = self._tile(x) self.codebook = out[:self.nb_code] self.code_sum = self.codebook.clone() self.code_count = torch.ones(self.nb_code, device=self.codebook.device) self.init = True @torch.no_grad() def compute_perplexity(self, code_idx) : # Calculate new centres code_onehot = torch.zeros(self.nb_code, code_idx.shape[0], device=code_idx.device) # nb_code, N * L code_onehot.scatter_(0, code_idx.view(1, code_idx.shape[0]), 1) code_count = code_onehot.sum(dim=-1) # nb_code prob = code_count / torch.sum(code_count) perplexity = torch.exp(-torch.sum(prob * torch.log(prob + 1e-7))) return perplexity @torch.no_grad() def update_codebook(self, x, code_idx): code_onehot = torch.zeros(self.nb_code, x.shape[0], device=x.device) # nb_code, N * L code_onehot.scatter_(0, code_idx.view(1, x.shape[0]), 1) code_sum = torch.matmul(code_onehot, x) # nb_code, w code_count = code_onehot.sum(dim=-1) # nb_code out = self._tile(x) code_rand = out[:self.nb_code] # Update centres self.code_sum = self.mu * self.code_sum + (1. - self.mu) * code_sum # w, nb_code self.code_count = self.mu * self.code_count + (1. - self.mu) * code_count # nb_code usage = (self.code_count.view(self.nb_code, 1) >= 1.0).float() code_update = self.code_sum.view(self.nb_code, self.code_dim) / self.code_count.view(self.nb_code, 1) self.codebook = usage * code_update + (1 - usage) * code_rand prob = code_count / torch.sum(code_count) perplexity = torch.exp(-torch.sum(prob * torch.log(prob + 1e-7))) return perplexity def preprocess(self, x): # NCT -> NTC -> [NT, C] x = x.permute(0, 2, 1).contiguous() x = x.view(-1, x.shape[-1]) return x def quantize(self, x): # Calculate latent code x_l k_w = self.codebook.t() distance = torch.sum(x ** 2, dim=-1, keepdim=True) - 2 * torch.matmul(x, k_w) + torch.sum(k_w ** 2, dim=0, keepdim=True) # (N * L, b) _, code_idx = torch.min(distance, dim=-1) return code_idx def dequantize(self, code_idx): x = F.embedding(code_idx, self.codebook) return x def forward(self, x): N, width, T = x.shape # Preprocess x = self.preprocess(x) # Init codebook if not inited if self.training and not self.init: self.init_codebook(x) # quantize and dequantize through bottleneck code_idx = self.quantize(x) x_d = self.dequantize(code_idx) # Update embeddings if self.training: perplexity = self.update_codebook(x, code_idx) else : perplexity = self.compute_perplexity(code_idx) # Loss commit_loss = F.mse_loss(x, x_d.detach()) # Passthrough x_d = x + (x_d - x).detach() # Postprocess x_d = x_d.view(N, T, -1).permute(0, 2, 1).contiguous() #(N, DIM, T) return x_d, commit_loss, perplexity class Quantizer(nn.Module): def __init__(self, n_e, e_dim, beta): super(Quantizer, self).__init__() self.e_dim = e_dim self.n_e = n_e self.beta = beta self.embedding = nn.Embedding(self.n_e, self.e_dim) self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) def forward(self, z): N, width, T = z.shape z = self.preprocess(z) assert z.shape[-1] == self.e_dim z_flattened = z.contiguous().view(-1, self.e_dim) # B x V d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ torch.sum(self.embedding.weight**2, dim=1) - 2 * \ torch.matmul(z_flattened, self.embedding.weight.t()) # B x 1 min_encoding_indices = torch.argmin(d, dim=1) z_q = self.embedding(min_encoding_indices).view(z.shape) # compute loss for embedding loss = torch.mean((z_q - z.detach())**2) + self.beta * \ torch.mean((z_q.detach() - z)**2) # preserve gradients z_q = z + (z_q - z).detach() z_q = z_q.view(N, T, -1).permute(0, 2, 1).contiguous() #(N, DIM, T) min_encodings = F.one_hot(min_encoding_indices, self.n_e).type(z.dtype) e_mean = torch.mean(min_encodings, dim=0) perplexity = torch.exp(-torch.sum(e_mean*torch.log(e_mean + 1e-10))) return z_q, loss, perplexity def quantize(self, z): assert z.shape[-1] == self.e_dim # B x V d = torch.sum(z ** 2, dim=1, keepdim=True) + \ torch.sum(self.embedding.weight ** 2, dim=1) - 2 * \ torch.matmul(z, self.embedding.weight.t()) # B x 1 min_encoding_indices = torch.argmin(d, dim=1) return min_encoding_indices def dequantize(self, indices): index_flattened = indices.view(-1) z_q = self.embedding(index_flattened) z_q = z_q.view(indices.shape + (self.e_dim, )).contiguous() return z_q def preprocess(self, x): # NCT -> NTC -> [NT, C] x = x.permute(0, 2, 1).contiguous() x = x.view(-1, x.shape[-1]) return x class QuantizeReset(nn.Module): def __init__(self, nb_code, code_dim): super().__init__() self.nb_code = nb_code self.code_dim = code_dim self.reset_codebook() self.codebook = nn.Parameter(torch.randn(nb_code, code_dim)) def reset_codebook(self): self.init = False self.code_count = None def _tile(self, x): nb_code_x, code_dim = x.shape if nb_code_x < self.nb_code: n_repeats = (self.nb_code + nb_code_x - 1) // nb_code_x std = 0.01 / np.sqrt(code_dim) out = x.repeat(n_repeats, 1) out = out + torch.randn_like(out) * std else : out = x return out def init_codebook(self, x): out = self._tile(x) self.codebook = nn.Parameter(out[:self.nb_code]) self.code_count = torch.ones(self.nb_code, device=self.codebook.device) self.init = True @torch.no_grad() def compute_perplexity(self, code_idx) : # Calculate new centres code_onehot = torch.zeros(self.nb_code, code_idx.shape[0], device=code_idx.device) # nb_code, N * L code_onehot.scatter_(0, code_idx.view(1, code_idx.shape[0]), 1) code_count = code_onehot.sum(dim=-1) # nb_code prob = code_count / torch.sum(code_count) perplexity = torch.exp(-torch.sum(prob * torch.log(prob + 1e-7))) return perplexity def update_codebook(self, x, code_idx): code_onehot = torch.zeros(self.nb_code, x.shape[0], device=x.device) # nb_code, N * L code_onehot.scatter_(0, code_idx.view(1, x.shape[0]), 1) code_count = code_onehot.sum(dim=-1) # nb_code out = self._tile(x) code_rand = out[:self.nb_code] # Update centres self.code_count = code_count # nb_code usage = (self.code_count.view(self.nb_code, 1) >= 1.0).float() self.codebook.data = usage * self.codebook.data + (1 - usage) * code_rand prob = code_count / torch.sum(code_count) perplexity = torch.exp(-torch.sum(prob * torch.log(prob + 1e-7))) return perplexity def preprocess(self, x): # NCT -> NTC -> [NT, C] x = x.permute(0, 2, 1).contiguous() x = x.view(-1, x.shape[-1]) return x def quantize(self, x): # Calculate latent code x_l k_w = self.codebook.t() distance = torch.sum(x ** 2, dim=-1, keepdim=True) - 2 * torch.matmul(x, k_w) + torch.sum(k_w ** 2, dim=0, keepdim=True) # (N * L, b) _, code_idx = torch.min(distance, dim=-1) return code_idx def dequantize(self, code_idx): x = F.embedding(code_idx, self.codebook) return x def forward(self, x): N, width, T = x.shape # Preprocess x = self.preprocess(x) # Init codebook if not inited if self.training and not self.init: self.init_codebook(x) # quantize and dequantize through bottleneck code_idx = self.quantize(x) x_d = self.dequantize(code_idx) # Update embeddings if self.training: perplexity = self.update_codebook(x, code_idx) else : perplexity = self.compute_perplexity(code_idx) # Loss commit_loss = F.mse_loss(x, x_d.detach()) # Passthrough x_d = x + (x_d - x).detach() # Postprocess x_d = x_d.view(N, T, -1).permute(0, 2, 1).contiguous() #(N, DIM, T) return x_d, commit_loss, perplexity class QuantizeEMA(nn.Module): def __init__(self, nb_code, code_dim, mu): super().__init__() self.nb_code = nb_code self.code_dim = code_dim self.mu = mu self.reset_codebook() def reset_codebook(self): self.init = False self.code_sum = None self.code_count = None self.register_buffer('codebook', torch.zeros(self.nb_code, self.code_dim).cuda()) def _tile(self, x): nb_code_x, code_dim = x.shape if nb_code_x < self.nb_code: n_repeats = (self.nb_code + nb_code_x - 1) // nb_code_x std = 0.01 / np.sqrt(code_dim) out = x.repeat(n_repeats, 1) out = out + torch.randn_like(out) * std else : out = x return out def init_codebook(self, x): out = self._tile(x) self.codebook = out[:self.nb_code] self.code_sum = self.codebook.clone() self.code_count = torch.ones(self.nb_code, device=self.codebook.device) self.init = True @torch.no_grad() def compute_perplexity(self, code_idx) : # Calculate new centres code_onehot = torch.zeros(self.nb_code, code_idx.shape[0], device=code_idx.device) # nb_code, N * L code_onehot.scatter_(0, code_idx.view(1, code_idx.shape[0]), 1) code_count = code_onehot.sum(dim=-1) # nb_code prob = code_count / torch.sum(code_count) perplexity = torch.exp(-torch.sum(prob * torch.log(prob + 1e-7))) return perplexity @torch.no_grad() def update_codebook(self, x, code_idx): code_onehot = torch.zeros(self.nb_code, x.shape[0], device=x.device) # nb_code, N * L code_onehot.scatter_(0, code_idx.view(1, x.shape[0]), 1) code_sum = torch.matmul(code_onehot, x) # nb_code, w code_count = code_onehot.sum(dim=-1) # nb_code # Update centres self.code_sum = self.mu * self.code_sum + (1. - self.mu) * code_sum # w, nb_code self.code_count = self.mu * self.code_count + (1. - self.mu) * code_count # nb_code code_update = self.code_sum.view(self.nb_code, self.code_dim) / self.code_count.view(self.nb_code, 1) self.codebook = code_update prob = code_count / torch.sum(code_count) perplexity = torch.exp(-torch.sum(prob * torch.log(prob + 1e-7))) return perplexity def preprocess(self, x): # NCT -> NTC -> [NT, C] x = x.permute(0, 2, 1).contiguous() x = x.view(-1, x.shape[-1]) return x def quantize(self, x): # Calculate latent code x_l k_w = self.codebook.t() distance = torch.sum(x ** 2, dim=-1, keepdim=True) - 2 * torch.matmul(x, k_w) + torch.sum(k_w ** 2, dim=0, keepdim=True) # (N * L, b) _, code_idx = torch.min(distance, dim=-1) return code_idx def dequantize(self, code_idx): x = F.embedding(code_idx, self.codebook) return x def forward(self, x): N, width, T = x.shape # Preprocess x = self.preprocess(x) # Init codebook if not inited if self.training and not self.init: self.init_codebook(x) # quantize and dequantize through bottleneck code_idx = self.quantize(x) x_d = self.dequantize(code_idx) # Update embeddings if self.training: perplexity = self.update_codebook(x, code_idx) else : perplexity = self.compute_perplexity(code_idx) # Loss commit_loss = F.mse_loss(x, x_d.detach()) # Passthrough x_d = x + (x_d - x).detach() # Postprocess x_d = x_d.view(N, T, -1).permute(0, 2, 1).contiguous() #(N, DIM, T) return x_d, commit_loss, perplexity