329 lines
11 KiB
Python
329 lines
11 KiB
Python
# Copyright 2018 The Sonnet Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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# Borrowed from https://github.com/rosinality/vq-vae-2-pytorch
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# Which was itself borrowed from https://github.com/deepmind/sonnet
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import torch
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from torch import nn
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from torch.nn import functional as F
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import torch.distributed as distributed
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from trainer.networks import register_model
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from utils.util import checkpoint, opt_get
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class Quantize(nn.Module):
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def __init__(self, dim, n_embed, decay=0.99, eps=1e-5, balancing_heuristic=False):
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super().__init__()
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self.dim = dim
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self.n_embed = n_embed
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self.decay = decay
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self.eps = eps
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self.balancing_heuristic = balancing_heuristic
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self.codes = None
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self.max_codes = 64000
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self.codes_full = False
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embed = torch.randn(dim, n_embed)
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self.register_buffer("embed", embed)
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self.register_buffer("cluster_size", torch.zeros(n_embed))
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self.register_buffer("embed_avg", embed.clone())
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def forward(self, input, return_soft_codes=False):
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if self.balancing_heuristic and self.codes_full:
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h = torch.histc(self.codes, bins=self.n_embed, min=0, max=self.n_embed) / len(self.codes)
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mask = torch.logical_or(h > .9, h < .01).unsqueeze(1)
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ep = self.embed.permute(1,0)
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ea = self.embed_avg.permute(1,0)
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rand_embed = torch.randn_like(ep) * mask
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self.embed = (ep * ~mask + rand_embed).permute(1,0)
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self.embed_avg = (ea * ~mask + rand_embed).permute(1,0)
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self.cluster_size = self.cluster_size * ~mask.squeeze()
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if torch.any(mask):
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print(f"Reset {torch.sum(mask)} embedding codes.")
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self.codes = None
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self.codes_full = False
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flatten = input.reshape(-1, self.dim)
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dist = (
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flatten.pow(2).sum(1, keepdim=True)
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- 2 * flatten @ self.embed
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+ self.embed.pow(2).sum(0, keepdim=True)
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)
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soft_codes = -dist
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_, embed_ind = soft_codes.max(1)
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embed_onehot = F.one_hot(embed_ind, self.n_embed).type(flatten.dtype)
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embed_ind = embed_ind.view(*input.shape[:-1])
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quantize = self.embed_code(embed_ind)
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if self.balancing_heuristic:
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if self.codes is None:
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self.codes = embed_ind.flatten()
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else:
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self.codes = torch.cat([self.codes, embed_ind.flatten()])
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if len(self.codes) > self.max_codes:
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self.codes = self.codes[-self.max_codes:]
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self.codes_full = True
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if self.training:
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embed_onehot_sum = embed_onehot.sum(0)
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embed_sum = flatten.transpose(0, 1) @ embed_onehot
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if distributed.is_initialized() and distributed.get_world_size() > 1:
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distributed.all_reduce(embed_onehot_sum)
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distributed.all_reduce(embed_sum)
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self.cluster_size.data.mul_(self.decay).add_(
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embed_onehot_sum, alpha=1 - self.decay
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)
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self.embed_avg.data.mul_(self.decay).add_(embed_sum, alpha=1 - self.decay)
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n = self.cluster_size.sum()
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cluster_size = (
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(self.cluster_size + self.eps) / (n + self.n_embed * self.eps) * n
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)
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embed_normalized = self.embed_avg / cluster_size.unsqueeze(0)
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self.embed.data.copy_(embed_normalized)
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diff = (quantize.detach() - input).pow(2).mean()
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quantize = input + (quantize - input).detach()
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if return_soft_codes:
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return quantize, diff, embed_ind, soft_codes.view(input.shape[:-1] + (-1,))
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else:
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return quantize, diff, embed_ind
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def embed_code(self, embed_id):
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return F.embedding(embed_id, self.embed.transpose(0, 1))
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class ResBlock(nn.Module):
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def __init__(self, in_channel, channel, conv_module):
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super().__init__()
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self.conv = nn.Sequential(
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nn.ReLU(inplace=True),
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conv_module(in_channel, channel, 3, padding=1),
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nn.ReLU(inplace=True),
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conv_module(channel, in_channel, 1),
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)
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def forward(self, input):
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out = self.conv(input)
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out += input
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return out
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class Encoder(nn.Module):
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def __init__(self, in_channel, channel, n_res_block, n_res_channel, stride, conv_module):
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super().__init__()
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if stride == 4:
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blocks = [
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conv_module(in_channel, channel // 2, 4, stride=2, padding=1),
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nn.ReLU(inplace=True),
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conv_module(channel // 2, channel, 4, stride=2, padding=1),
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nn.ReLU(inplace=True),
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conv_module(channel, channel, 3, padding=1),
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]
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elif stride == 2:
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blocks = [
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conv_module(in_channel, channel // 2, 4, stride=2, padding=1),
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nn.ReLU(inplace=True),
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conv_module(channel // 2, channel, 3, padding=1),
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]
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for i in range(n_res_block):
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blocks.append(ResBlock(channel, n_res_channel, conv_module))
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blocks.append(nn.ReLU(inplace=True))
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self.blocks = nn.Sequential(*blocks)
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def forward(self, input):
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return self.blocks(input)
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class Decoder(nn.Module):
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def __init__(
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self, in_channel, out_channel, channel, n_res_block, n_res_channel, stride, conv_module, conv_transpose_module
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):
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super().__init__()
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blocks = [conv_module(in_channel, channel, 3, padding=1)]
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for i in range(n_res_block):
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blocks.append(ResBlock(channel, n_res_channel, conv_module))
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blocks.append(nn.ReLU(inplace=True))
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if stride == 4:
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blocks.extend(
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[
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conv_transpose_module(channel, channel // 2, 4, stride=2, padding=1),
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nn.ReLU(inplace=True),
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conv_transpose_module(
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channel // 2, out_channel, 4, stride=2, padding=1
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),
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]
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)
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elif stride == 2:
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blocks.append(
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conv_transpose_module(channel, out_channel, 4, stride=2, padding=1)
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)
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self.blocks = nn.Sequential(*blocks)
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def forward(self, input):
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return self.blocks(input)
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class VQVAE(nn.Module):
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def __init__(
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self,
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in_channel=3,
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channel=128,
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n_res_block=2,
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n_res_channel=32,
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codebook_dim=64,
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codebook_size=512,
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conv_module=nn.Conv2d,
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conv_transpose_module=nn.ConvTranspose2d,
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decay=0.99,
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):
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super().__init__()
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self.unsqueeze_channels = in_channel == -1
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in_channel = abs(in_channel)
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self.codebook_size = codebook_size
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self.enc_b = Encoder(in_channel, channel, n_res_block, n_res_channel, stride=4, conv_module=conv_module)
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self.enc_t = Encoder(channel, channel, n_res_block, n_res_channel, stride=2, conv_module=conv_module)
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self.quantize_conv_t = conv_module(channel, codebook_dim, 1)
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self.quantize_t = Quantize(codebook_dim, codebook_size)
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self.dec_t = Decoder(
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codebook_dim, codebook_dim, channel, n_res_block, n_res_channel, stride=2, conv_module=conv_module, conv_transpose_module=conv_transpose_module
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)
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self.quantize_conv_b = conv_module(codebook_dim + channel, codebook_dim, 1)
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self.quantize_b = Quantize(codebook_dim, codebook_size)
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self.upsample_t = conv_transpose_module(
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codebook_dim, codebook_dim, 4, stride=2, padding=1
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)
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self.dec = Decoder(
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codebook_dim + codebook_dim,
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in_channel,
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channel,
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n_res_block,
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n_res_channel,
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stride=4,
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conv_module=conv_module,
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conv_transpose_module=conv_transpose_module
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)
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def forward(self, input):
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if self.unsqueeze_channels:
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input = input.unsqueeze(1)
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quant_t, quant_b, diff, _, _ = self.encode(input)
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dec = self.decode(quant_t, quant_b)
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if self.unsqueeze_channels:
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dec = dec.squeeze(1)
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return dec, diff
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def encode(self, input):
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enc_b = checkpoint(self.enc_b, input)
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enc_t = checkpoint(self.enc_t, enc_b)
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quant_t = self.quantize_conv_t(enc_t).permute((0,2,3,1) if len(input.shape) == 4 else (0,2,1))
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quant_t, diff_t, id_t = self.quantize_t(quant_t)
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quant_t = quant_t.permute((0,3,1,2) if len(input.shape) == 4 else (0,2,1))
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diff_t = diff_t.unsqueeze(0)
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dec_t = checkpoint(self.dec_t, quant_t)
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enc_b = torch.cat([dec_t, enc_b], 1)
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quant_b = checkpoint(self.quantize_conv_b, enc_b).permute((0,2,3,1) if len(input.shape) == 4 else (0,2,1))
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quant_b, diff_b, id_b = self.quantize_b(quant_b)
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quant_b = quant_b.permute((0,3,1,2) if len(input.shape) == 4 else (0,2,1))
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diff_b = diff_b.unsqueeze(0)
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return quant_t, quant_b, diff_t + diff_b, id_t, id_b
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def encode_only_quantized(self, input):
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qt, qb, d, idt, idb = self.encode(input)
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# Append top and bottom into the same sequence, adding the codebook length onto the top to discriminate it.
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idt += self.codebook_size
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ids = torch.cat([idt, idb], dim=1)
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return ids
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def decode(self, quant_t, quant_b):
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upsample_t = self.upsample_t(quant_t)
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quant = torch.cat([upsample_t, quant_b], 1)
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dec = checkpoint(self.dec, quant)
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return dec
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def decode_code(self, code_t, code_b):
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quant_t = self.quantize_t.embed_code(code_t)
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quant_t = quant_t.permute((0,3,1,2) if len(code_t.shape) == 4 else (0,2,1))
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quant_b = self.quantize_b.embed_code(code_b)
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quant_b = quant_b.permute((0,3,1,2) if len(code_t.shape) == 4 else (0,2,1))
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dec = self.decode(quant_t, quant_b)
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return dec
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# Performs decode_code() with the outputs from encode_only_quantized.
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def decode_code_joined(self, input):
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b, s = input.shape
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assert s % 3 == 0 # If not, this tensor didn't come from encode_only_quantized.
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s = s // 3
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# This doesn't work with batching. TODO: fixme.
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t = input[:,:s] - self.codebook_size
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b = input[:,s:]
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return self.decode_code(t, b)
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@register_model
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def register_vqvae(opt_net, opt):
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kw = opt_get(opt_net, ['kwargs'], {})
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vq = VQVAE(**kw)
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return vq
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@register_model
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def register_vqvae_audio(opt_net, opt):
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kw = opt_get(opt_net, ['kwargs'], {})
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kw['conv_module'] = nn.Conv1d
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kw['conv_transpose_module'] = nn.ConvTranspose1d
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vq = VQVAE(**kw)
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return vq
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if __name__ == '__main__':
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model = VQVAE(in_channel=80, conv_module=nn.Conv1d, conv_transpose_module=nn.ConvTranspose1d)
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#res=model(torch.randn(1,80,2048))
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e = model.encode_only_quantized(torch.randn(1, 80, 2048))
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k = model.decode_code_joined(e)
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print(k.shape) |