This commit is contained in:
James Betker 2021-01-07 10:20:15 -07:00
parent 01a589e712
commit 61a86a3c1e
4 changed files with 254 additions and 5 deletions

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codes/models/vqvae/vqvae.py Normal file
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# 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 orrowed 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):
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):
super().__init__()
self.conv = nn.Sequential(
nn.ReLU(inplace=True),
nn.Conv2d(in_channel, channel, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(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):
super().__init__()
if stride == 4:
blocks = [
nn.Conv2d(in_channel, channel // 2, 4, stride=2, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(channel // 2, channel, 4, stride=2, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(channel, channel, 3, padding=1),
]
elif stride == 2:
blocks = [
nn.Conv2d(in_channel, channel // 2, 4, stride=2, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(channel // 2, channel, 3, padding=1),
]
for i in range(n_res_block):
blocks.append(ResBlock(channel, n_res_channel))
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
):
super().__init__()
blocks = [nn.Conv2d(in_channel, channel, 3, padding=1)]
for i in range(n_res_block):
blocks.append(ResBlock(channel, n_res_channel))
blocks.append(nn.ReLU(inplace=True))
if stride == 4:
blocks.extend(
[
nn.ConvTranspose2d(channel, channel // 2, 4, stride=2, padding=1),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(
channel // 2, out_channel, 4, stride=2, padding=1
),
]
)
elif stride == 2:
blocks.append(
nn.ConvTranspose2d(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,
decay=0.99,
):
super().__init__()
self.enc_b = Encoder(in_channel, channel, n_res_block, n_res_channel, stride=4)
self.enc_t = Encoder(channel, channel, n_res_block, n_res_channel, stride=2)
self.quantize_conv_t = nn.Conv2d(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
)
self.quantize_conv_b = nn.Conv2d(codebook_dim + channel, codebook_dim, 1)
self.quantize_b = Quantize(codebook_dim, codebook_size)
self.upsample_t = nn.ConvTranspose2d(
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,
)
def forward(self, input):
quant_t, quant_b, diff, _, _ = self.encode(input)
dec = self.decode(quant_t, quant_b)
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)
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 = checkpoint(self.quantize_conv_b, enc_b).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):
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)
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)
return dec
@register_model
def register_vqvae(opt_net, opt):
kw = opt_get(opt_net, ['kwargs'], {})
return VQVAE(**kw)

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@ -14,14 +14,14 @@ def main():
split_img = False
opt = {}
opt['n_thread'] = 4
opt['compression_level'] = 90 # JPEG compression quality rating.
opt['compression_level'] = 98 # JPEG compression quality rating.
# CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
# compression time. If read raw images during training, use 0 for faster IO speed.
opt['dest'] = 'file'
opt['input_folder'] = ['F:\\4k6k\\datasets\\ns_images\\vixen\\vix_cropped']
opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\video_512_cropped'
opt['imgsize'] = 512
opt['input_folder'] = ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_1024_square_with_new']
opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_256_full'
opt['imgsize'] = 256
#opt['bottom_crop'] = 120
save_folder = opt['save_folder']

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@ -295,7 +295,7 @@ class Trainer:
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_pixpro_resnet.yml')
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_stylesr.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()