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# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py | |
''' | |
VQGAN code, adapted from the original created by the Unleashing Transformers authors: | |
https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py | |
''' | |
import numpy as np | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import copy | |
from basicsr.utils import get_root_logger | |
from basicsr.utils.registry import ARCH_REGISTRY | |
def normalize(in_channels): | |
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) | |
def swish(x): | |
return x*torch.sigmoid(x) | |
# Define VQVAE classes | |
class VectorQuantizer(nn.Module): | |
def __init__(self, codebook_size, emb_dim, beta): | |
super(VectorQuantizer, self).__init__() | |
self.codebook_size = codebook_size # number of embeddings | |
self.emb_dim = emb_dim # dimension of embedding | |
self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 | |
self.embedding = nn.Embedding(self.codebook_size, self.emb_dim) | |
self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size) | |
def forward(self, z): | |
# reshape z -> (batch, height, width, channel) and flatten | |
z = z.permute(0, 2, 3, 1).contiguous() | |
z_flattened = z.view(-1, self.emb_dim) | |
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z | |
d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \ | |
2 * torch.matmul(z_flattened, self.embedding.weight.t()) | |
mean_distance = torch.mean(d) | |
# find closest encodings | |
# min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) | |
min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False) | |
# [0-1], higher score, higher confidence | |
min_encoding_scores = torch.exp(-min_encoding_scores/10) | |
min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z) | |
min_encodings.scatter_(1, min_encoding_indices, 1) | |
# get quantized latent vectors | |
z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) | |
# compute loss for embedding | |
loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2) | |
# preserve gradients | |
z_q = z + (z_q - z).detach() | |
# perplexity | |
e_mean = torch.mean(min_encodings, dim=0) | |
perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) | |
# reshape back to match original input shape | |
z_q = z_q.permute(0, 3, 1, 2).contiguous() | |
return z_q, loss, { | |
"perplexity": perplexity, | |
"min_encodings": min_encodings, | |
"min_encoding_indices": min_encoding_indices, | |
"min_encoding_scores": min_encoding_scores, | |
"mean_distance": mean_distance | |
} | |
def get_codebook_feat(self, indices, shape): | |
# input indices: batch*token_num -> (batch*token_num)*1 | |
# shape: batch, height, width, channel | |
indices = indices.view(-1,1) | |
min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices) | |
min_encodings.scatter_(1, indices, 1) | |
# get quantized latent vectors | |
z_q = torch.matmul(min_encodings.float(), self.embedding.weight) | |
if shape is not None: # reshape back to match original input shape | |
z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous() | |
return z_q | |
class GumbelQuantizer(nn.Module): | |
def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0): | |
super().__init__() | |
self.codebook_size = codebook_size # number of embeddings | |
self.emb_dim = emb_dim # dimension of embedding | |
self.straight_through = straight_through | |
self.temperature = temp_init | |
self.kl_weight = kl_weight | |
self.proj = nn.Conv2d(num_hiddens, codebook_size, 1) # projects last encoder layer to quantized logits | |
self.embed = nn.Embedding(codebook_size, emb_dim) | |
def forward(self, z): | |
hard = self.straight_through if self.training else True | |
logits = self.proj(z) | |
soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard) | |
z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight) | |
# + kl divergence to the prior loss | |
qy = F.softmax(logits, dim=1) | |
diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean() | |
min_encoding_indices = soft_one_hot.argmax(dim=1) | |
return z_q, diff, { | |
"min_encoding_indices": min_encoding_indices | |
} | |
class Downsample(nn.Module): | |
def __init__(self, in_channels): | |
super().__init__() | |
self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) | |
def forward(self, x): | |
pad = (0, 1, 0, 1) | |
x = torch.nn.functional.pad(x, pad, mode="constant", value=0) | |
x = self.conv(x) | |
return x | |
class Upsample(nn.Module): | |
def __init__(self, in_channels): | |
super().__init__() | |
self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) | |
def forward(self, x): | |
x = F.interpolate(x, scale_factor=2.0, mode="nearest") | |
x = self.conv(x) | |
return x | |
class ResBlock(nn.Module): | |
def __init__(self, in_channels, out_channels=None): | |
super(ResBlock, self).__init__() | |
self.in_channels = in_channels | |
self.out_channels = in_channels if out_channels is None else out_channels | |
self.norm1 = normalize(in_channels) | |
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) | |
self.norm2 = normalize(out_channels) | |
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) | |
if self.in_channels != self.out_channels: | |
self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) | |
def forward(self, x_in): | |
x = x_in | |
x = self.norm1(x) | |
x = swish(x) | |
x = self.conv1(x) | |
x = self.norm2(x) | |
x = swish(x) | |
x = self.conv2(x) | |
if self.in_channels != self.out_channels: | |
x_in = self.conv_out(x_in) | |
return x + x_in | |
class AttnBlock(nn.Module): | |
def __init__(self, in_channels): | |
super().__init__() | |
self.in_channels = in_channels | |
self.norm = normalize(in_channels) | |
self.q = torch.nn.Conv2d( | |
in_channels, | |
in_channels, | |
kernel_size=1, | |
stride=1, | |
padding=0 | |
) | |
self.k = torch.nn.Conv2d( | |
in_channels, | |
in_channels, | |
kernel_size=1, | |
stride=1, | |
padding=0 | |
) | |
self.v = torch.nn.Conv2d( | |
in_channels, | |
in_channels, | |
kernel_size=1, | |
stride=1, | |
padding=0 | |
) | |
self.proj_out = torch.nn.Conv2d( | |
in_channels, | |
in_channels, | |
kernel_size=1, | |
stride=1, | |
padding=0 | |
) | |
def forward(self, x): | |
h_ = x | |
h_ = self.norm(h_) | |
q = self.q(h_) | |
k = self.k(h_) | |
v = self.v(h_) | |
# compute attention | |
b, c, h, w = q.shape | |
q = q.reshape(b, c, h*w) | |
q = q.permute(0, 2, 1) | |
k = k.reshape(b, c, h*w) | |
w_ = torch.bmm(q, k) | |
w_ = w_ * (int(c)**(-0.5)) | |
w_ = F.softmax(w_, dim=2) | |
# attend to values | |
v = v.reshape(b, c, h*w) | |
w_ = w_.permute(0, 2, 1) | |
h_ = torch.bmm(v, w_) | |
h_ = h_.reshape(b, c, h, w) | |
h_ = self.proj_out(h_) | |
return x+h_ | |
class Encoder(nn.Module): | |
def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions): | |
super().__init__() | |
self.nf = nf | |
self.num_resolutions = len(ch_mult) | |
self.num_res_blocks = num_res_blocks | |
self.resolution = resolution | |
self.attn_resolutions = attn_resolutions | |
curr_res = self.resolution | |
in_ch_mult = (1,)+tuple(ch_mult) | |
blocks = [] | |
# initial convultion | |
blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1)) | |
# residual and downsampling blocks, with attention on smaller res (16x16) | |
for i in range(self.num_resolutions): | |
block_in_ch = nf * in_ch_mult[i] | |
block_out_ch = nf * ch_mult[i] | |
for _ in range(self.num_res_blocks): | |
blocks.append(ResBlock(block_in_ch, block_out_ch)) | |
block_in_ch = block_out_ch | |
if curr_res in attn_resolutions: | |
blocks.append(AttnBlock(block_in_ch)) | |
if i != self.num_resolutions - 1: | |
blocks.append(Downsample(block_in_ch)) | |
curr_res = curr_res // 2 | |
# non-local attention block | |
blocks.append(ResBlock(block_in_ch, block_in_ch)) | |
blocks.append(AttnBlock(block_in_ch)) | |
blocks.append(ResBlock(block_in_ch, block_in_ch)) | |
# normalise and convert to latent size | |
blocks.append(normalize(block_in_ch)) | |
blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1)) | |
self.blocks = nn.ModuleList(blocks) | |
def forward(self, x): | |
for block in self.blocks: | |
x = block(x) | |
return x | |
class Generator(nn.Module): | |
def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions): | |
super().__init__() | |
self.nf = nf | |
self.ch_mult = ch_mult | |
self.num_resolutions = len(self.ch_mult) | |
self.num_res_blocks = res_blocks | |
self.resolution = img_size | |
self.attn_resolutions = attn_resolutions | |
self.in_channels = emb_dim | |
self.out_channels = 3 | |
block_in_ch = self.nf * self.ch_mult[-1] | |
curr_res = self.resolution // 2 ** (self.num_resolutions-1) | |
blocks = [] | |
# initial conv | |
blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)) | |
# non-local attention block | |
blocks.append(ResBlock(block_in_ch, block_in_ch)) | |
blocks.append(AttnBlock(block_in_ch)) | |
blocks.append(ResBlock(block_in_ch, block_in_ch)) | |
for i in reversed(range(self.num_resolutions)): | |
block_out_ch = self.nf * self.ch_mult[i] | |
for _ in range(self.num_res_blocks): | |
blocks.append(ResBlock(block_in_ch, block_out_ch)) | |
block_in_ch = block_out_ch | |
if curr_res in self.attn_resolutions: | |
blocks.append(AttnBlock(block_in_ch)) | |
if i != 0: | |
blocks.append(Upsample(block_in_ch)) | |
curr_res = curr_res * 2 | |
blocks.append(normalize(block_in_ch)) | |
blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1)) | |
self.blocks = nn.ModuleList(blocks) | |
def forward(self, x): | |
for block in self.blocks: | |
x = block(x) | |
return x | |
class VQAutoEncoder(nn.Module): | |
def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256, | |
beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None): | |
super().__init__() | |
logger = get_root_logger() | |
self.in_channels = 3 | |
self.nf = nf | |
self.n_blocks = res_blocks | |
self.codebook_size = codebook_size | |
self.embed_dim = emb_dim | |
self.ch_mult = ch_mult | |
self.resolution = img_size | |
self.attn_resolutions = attn_resolutions | |
self.quantizer_type = quantizer | |
self.encoder = Encoder( | |
self.in_channels, | |
self.nf, | |
self.embed_dim, | |
self.ch_mult, | |
self.n_blocks, | |
self.resolution, | |
self.attn_resolutions | |
) | |
if self.quantizer_type == "nearest": | |
self.beta = beta #0.25 | |
self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta) | |
elif self.quantizer_type == "gumbel": | |
self.gumbel_num_hiddens = emb_dim | |
self.straight_through = gumbel_straight_through | |
self.kl_weight = gumbel_kl_weight | |
self.quantize = GumbelQuantizer( | |
self.codebook_size, | |
self.embed_dim, | |
self.gumbel_num_hiddens, | |
self.straight_through, | |
self.kl_weight | |
) | |
self.generator = Generator( | |
self.nf, | |
self.embed_dim, | |
self.ch_mult, | |
self.n_blocks, | |
self.resolution, | |
self.attn_resolutions | |
) | |
if model_path is not None: | |
chkpt = torch.load(model_path, map_location='cpu') | |
if 'params_ema' in chkpt: | |
self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema']) | |
logger.info(f'vqgan is loaded from: {model_path} [params_ema]') | |
elif 'params' in chkpt: | |
self.load_state_dict(torch.load(model_path, map_location='cpu')['params']) | |
logger.info(f'vqgan is loaded from: {model_path} [params]') | |
else: | |
raise ValueError(f'Wrong params!') | |
def forward(self, x): | |
x = self.encoder(x) | |
quant, codebook_loss, quant_stats = self.quantize(x) | |
x = self.generator(quant) | |
return x, codebook_loss, quant_stats | |
# patch based discriminator | |
class VQGANDiscriminator(nn.Module): | |
def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None): | |
super().__init__() | |
layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)] | |
ndf_mult = 1 | |
ndf_mult_prev = 1 | |
for n in range(1, n_layers): # gradually increase the number of filters | |
ndf_mult_prev = ndf_mult | |
ndf_mult = min(2 ** n, 8) | |
layers += [ | |
nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False), | |
nn.BatchNorm2d(ndf * ndf_mult), | |
nn.LeakyReLU(0.2, True) | |
] | |
ndf_mult_prev = ndf_mult | |
ndf_mult = min(2 ** n_layers, 8) | |
layers += [ | |
nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False), | |
nn.BatchNorm2d(ndf * ndf_mult), | |
nn.LeakyReLU(0.2, True) | |
] | |
layers += [ | |
nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)] # output 1 channel prediction map | |
self.main = nn.Sequential(*layers) | |
if model_path is not None: | |
chkpt = torch.load(model_path, map_location='cpu') | |
if 'params_d' in chkpt: | |
self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d']) | |
elif 'params' in chkpt: | |
self.load_state_dict(torch.load(model_path, map_location='cpu')['params']) | |
else: | |
raise ValueError(f'Wrong params!') | |
def forward(self, x): | |
return self.main(x) |