268 lines
9.2 KiB
Python
268 lines
9.2 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.
|
|
# ============================================================================
|
|
|
|
import torch
|
|
from torch import nn
|
|
from torch.nn import functional as F
|
|
|
|
import torch.distributed as distributed
|
|
|
|
from models.vqvae.scaled_weight_conv import ScaledWeightConv, ScaledWeightConvTranspose
|
|
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):
|
|
super().__init__()
|
|
|
|
self.dim = dim
|
|
self.n_embed = n_embed
|
|
self.decay = decay
|
|
self.eps = eps
|
|
|
|
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):
|
|
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)
|
|
)
|
|
_, embed_ind = (-dist).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.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()
|
|
|
|
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, breadth):
|
|
super().__init__()
|
|
|
|
self.conv = nn.ModuleList([
|
|
nn.ReLU(inplace=True),
|
|
ScaledWeightConv(in_channel, channel, 3, padding=1, breadth=breadth),
|
|
nn.ReLU(inplace=True),
|
|
ScaledWeightConv(channel, in_channel, 1, breadth=breadth),
|
|
])
|
|
|
|
def forward(self, input, masks):
|
|
out = input
|
|
for m in self.conv:
|
|
if isinstance(m, ScaledWeightConv):
|
|
out = m(out, masks)
|
|
else:
|
|
out = m(out)
|
|
out += input
|
|
|
|
return out
|
|
|
|
|
|
class Encoder(nn.Module):
|
|
def __init__(self, in_channel, channel, n_res_block, n_res_channel, stride, breadth):
|
|
super().__init__()
|
|
|
|
if stride == 4:
|
|
blocks = [
|
|
ScaledWeightConv(in_channel, channel // 2, 4, stride=2, padding=1, breadth=breadth),
|
|
nn.ReLU(inplace=True),
|
|
ScaledWeightConv(channel // 2, channel, 4, stride=2, padding=1, breadth=breadth),
|
|
nn.ReLU(inplace=True),
|
|
ScaledWeightConv(channel, channel, 3, padding=1, breadth=breadth),
|
|
]
|
|
|
|
elif stride == 2:
|
|
blocks = [
|
|
ScaledWeightConv(in_channel, channel // 2, 4, stride=2, padding=1, breadth=breadth),
|
|
nn.ReLU(inplace=True),
|
|
ScaledWeightConv(channel // 2, channel, 3, padding=1, breadth=breadth),
|
|
]
|
|
|
|
for i in range(n_res_block):
|
|
blocks.append(ResBlock(channel, n_res_channel, breadth=breadth))
|
|
|
|
blocks.append(nn.ReLU(inplace=True))
|
|
|
|
self.blocks = nn.ModuleList(blocks)
|
|
|
|
def forward(self, input):
|
|
for block in self.blocks:
|
|
if isinstance(block, ScaledWeightConv) or isinstance(block, ResBlock):
|
|
input = block(input, self.masks)
|
|
else:
|
|
input = block(input)
|
|
return input
|
|
|
|
|
|
class Decoder(nn.Module):
|
|
def __init__(
|
|
self, in_channel, out_channel, channel, n_res_block, n_res_channel, stride, breadth
|
|
):
|
|
super().__init__()
|
|
|
|
blocks = [ScaledWeightConv(in_channel, channel, 3, padding=1, breadth=breadth)]
|
|
|
|
for i in range(n_res_block):
|
|
blocks.append(ResBlock(channel, n_res_channel, breadth=breadth))
|
|
|
|
blocks.append(nn.ReLU(inplace=True))
|
|
|
|
if stride == 4:
|
|
blocks.extend(
|
|
[
|
|
ScaledWeightConvTranspose(channel, channel // 2, 4, stride=2, padding=1, breadth=breadth),
|
|
nn.ReLU(inplace=True),
|
|
ScaledWeightConvTranspose(
|
|
channel // 2, out_channel, 4, stride=2, padding=1, breadth=breadth
|
|
),
|
|
]
|
|
)
|
|
|
|
elif stride == 2:
|
|
blocks.append(
|
|
ScaledWeightConvTranspose(channel, out_channel, 4, stride=2, padding=1, breadth=breadth)
|
|
)
|
|
|
|
self.blocks = nn.ModuleList(blocks)
|
|
|
|
def forward(self, input):
|
|
for block in self.blocks:
|
|
if isinstance(block, ScaledWeightConvTranspose) or isinstance(block, ResBlock) \
|
|
or isinstance(block, ScaledWeightConv):
|
|
input = block(input, self.masks)
|
|
else:
|
|
input = block(input)
|
|
return 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,
|
|
breadth=8,
|
|
decay=0.99,
|
|
):
|
|
super().__init__()
|
|
|
|
self.enc_b = Encoder(in_channel, channel, n_res_block, n_res_channel, stride=4, breadth=breadth)
|
|
self.enc_t = Encoder(channel, channel, n_res_block, n_res_channel, stride=2, breadth=breadth)
|
|
self.quantize_conv_t = ScaledWeightConv(channel, codebook_dim, 1, breadth=breadth)
|
|
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, breadth=breadth
|
|
)
|
|
self.quantize_conv_b = ScaledWeightConv(codebook_dim + channel, codebook_dim, 1, breadth=breadth)
|
|
self.quantize_b = Quantize(codebook_dim, codebook_size)
|
|
self.upsample_t = ScaledWeightConvTranspose(
|
|
codebook_dim, codebook_dim, 4, stride=2, padding=1, breadth=breadth
|
|
)
|
|
self.dec = Decoder(
|
|
codebook_dim + codebook_dim,
|
|
in_channel,
|
|
channel,
|
|
n_res_block,
|
|
n_res_channel,
|
|
stride=4,
|
|
breadth=breadth
|
|
)
|
|
|
|
def forward(self, input, masks):
|
|
# This awkward injection point is necessary to enable checkpointing to work.
|
|
for m in [self.enc_b, self.enc_t, self.dec_t, self.dec]:
|
|
m.masks = masks
|
|
|
|
quant_t, quant_b, diff, _, _ = self.encode(input, masks)
|
|
dec = self.decode(quant_t, quant_b, masks)
|
|
|
|
return dec, diff
|
|
|
|
def encode(self, input, masks):
|
|
enc_b = checkpoint(self.enc_b, input)
|
|
enc_t = checkpoint(self.enc_t, enc_b)
|
|
|
|
quant_t = self.quantize_conv_t(enc_t, masks).permute(0, 2, 3, 1)
|
|
quant_t, diff_t, id_t = self.quantize_t(quant_t)
|
|
quant_t = quant_t.permute(0, 3, 1, 2)
|
|
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 = self.quantize_conv_b(enc_b, masks).permute(0, 2, 3, 1)
|
|
quant_b, diff_b, id_b = self.quantize_b(quant_b)
|
|
quant_b = quant_b.permute(0, 3, 1, 2)
|
|
diff_b = diff_b.unsqueeze(0)
|
|
|
|
return quant_t, quant_b, diff_t + diff_b, id_t, id_b
|
|
|
|
def decode(self, quant_t, quant_b, masks):
|
|
upsample_t = self.upsample_t(quant_t, masks)
|
|
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)
|
|
quant_b = self.quantize_b.embed_code(code_b)
|
|
quant_b = quant_b.permute(0, 3, 1, 2)
|
|
|
|
dec = self.decode(quant_t, quant_b, masks)
|
|
|
|
return dec
|
|
|
|
|
|
@register_model
|
|
def register_weighted_vqvae(opt_net, opt):
|
|
kw = opt_get(opt_net, ['kwargs'], {})
|
|
return VQVAE(**kw)
|