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