277 lines
11 KiB
Python
277 lines
11 KiB
Python
# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
|
|
|
|
import math
|
|
import torch
|
|
from torch import nn, Tensor
|
|
import torch.nn.functional as F
|
|
from typing import Optional
|
|
|
|
from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
|
|
from basicsr.utils.registry import ARCH_REGISTRY
|
|
|
|
def calc_mean_std(feat, eps=1e-5):
|
|
"""Calculate mean and std for adaptive_instance_normalization.
|
|
|
|
Args:
|
|
feat (Tensor): 4D tensor.
|
|
eps (float): A small value added to the variance to avoid
|
|
divide-by-zero. Default: 1e-5.
|
|
"""
|
|
size = feat.size()
|
|
assert len(size) == 4, 'The input feature should be 4D tensor.'
|
|
b, c = size[:2]
|
|
feat_var = feat.view(b, c, -1).var(dim=2) + eps
|
|
feat_std = feat_var.sqrt().view(b, c, 1, 1)
|
|
feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
|
|
return feat_mean, feat_std
|
|
|
|
|
|
def adaptive_instance_normalization(content_feat, style_feat):
|
|
"""Adaptive instance normalization.
|
|
|
|
Adjust the reference features to have the similar color and illuminations
|
|
as those in the degradate features.
|
|
|
|
Args:
|
|
content_feat (Tensor): The reference feature.
|
|
style_feat (Tensor): The degradate features.
|
|
"""
|
|
size = content_feat.size()
|
|
style_mean, style_std = calc_mean_std(style_feat)
|
|
content_mean, content_std = calc_mean_std(content_feat)
|
|
normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
|
|
return normalized_feat * style_std.expand(size) + style_mean.expand(size)
|
|
|
|
|
|
class PositionEmbeddingSine(nn.Module):
|
|
"""
|
|
This is a more standard version of the position embedding, very similar to the one
|
|
used by the Attention is all you need paper, generalized to work on images.
|
|
"""
|
|
|
|
def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
|
|
super().__init__()
|
|
self.num_pos_feats = num_pos_feats
|
|
self.temperature = temperature
|
|
self.normalize = normalize
|
|
if scale is not None and normalize is False:
|
|
raise ValueError("normalize should be True if scale is passed")
|
|
if scale is None:
|
|
scale = 2 * math.pi
|
|
self.scale = scale
|
|
|
|
def forward(self, x, mask=None):
|
|
if mask is None:
|
|
mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
|
|
not_mask = ~mask
|
|
y_embed = not_mask.cumsum(1, dtype=torch.float32)
|
|
x_embed = not_mask.cumsum(2, dtype=torch.float32)
|
|
if self.normalize:
|
|
eps = 1e-6
|
|
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
|
|
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
|
|
|
|
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
|
|
dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
|
|
|
|
pos_x = x_embed[:, :, :, None] / dim_t
|
|
pos_y = y_embed[:, :, :, None] / dim_t
|
|
pos_x = torch.stack(
|
|
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
|
|
).flatten(3)
|
|
pos_y = torch.stack(
|
|
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
|
|
).flatten(3)
|
|
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
|
|
return pos
|
|
|
|
def _get_activation_fn(activation):
|
|
"""Return an activation function given a string"""
|
|
if activation == "relu":
|
|
return F.relu
|
|
if activation == "gelu":
|
|
return F.gelu
|
|
if activation == "glu":
|
|
return F.glu
|
|
raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
|
|
|
|
|
|
class TransformerSALayer(nn.Module):
|
|
def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"):
|
|
super().__init__()
|
|
self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)
|
|
# Implementation of Feedforward model - MLP
|
|
self.linear1 = nn.Linear(embed_dim, dim_mlp)
|
|
self.dropout = nn.Dropout(dropout)
|
|
self.linear2 = nn.Linear(dim_mlp, embed_dim)
|
|
|
|
self.norm1 = nn.LayerNorm(embed_dim)
|
|
self.norm2 = nn.LayerNorm(embed_dim)
|
|
self.dropout1 = nn.Dropout(dropout)
|
|
self.dropout2 = nn.Dropout(dropout)
|
|
|
|
self.activation = _get_activation_fn(activation)
|
|
|
|
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
|
|
return tensor if pos is None else tensor + pos
|
|
|
|
def forward(self, tgt,
|
|
tgt_mask: Optional[Tensor] = None,
|
|
tgt_key_padding_mask: Optional[Tensor] = None,
|
|
query_pos: Optional[Tensor] = None):
|
|
|
|
# self attention
|
|
tgt2 = self.norm1(tgt)
|
|
q = k = self.with_pos_embed(tgt2, query_pos)
|
|
tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
|
|
key_padding_mask=tgt_key_padding_mask)[0]
|
|
tgt = tgt + self.dropout1(tgt2)
|
|
|
|
# ffn
|
|
tgt2 = self.norm2(tgt)
|
|
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
|
|
tgt = tgt + self.dropout2(tgt2)
|
|
return tgt
|
|
|
|
class Fuse_sft_block(nn.Module):
|
|
def __init__(self, in_ch, out_ch):
|
|
super().__init__()
|
|
self.encode_enc = ResBlock(2*in_ch, out_ch)
|
|
|
|
self.scale = nn.Sequential(
|
|
nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
|
|
nn.LeakyReLU(0.2, True),
|
|
nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
|
|
|
|
self.shift = nn.Sequential(
|
|
nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
|
|
nn.LeakyReLU(0.2, True),
|
|
nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
|
|
|
|
def forward(self, enc_feat, dec_feat, w=1):
|
|
enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))
|
|
scale = self.scale(enc_feat)
|
|
shift = self.shift(enc_feat)
|
|
residual = w * (dec_feat * scale + shift)
|
|
out = dec_feat + residual
|
|
return out
|
|
|
|
|
|
@ARCH_REGISTRY.register()
|
|
class CodeFormer(VQAutoEncoder):
|
|
def __init__(self, dim_embd=512, n_head=8, n_layers=9,
|
|
codebook_size=1024, latent_size=256,
|
|
connect_list=('32', '64', '128', '256'),
|
|
fix_modules=('quantize', 'generator')):
|
|
super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)
|
|
|
|
if fix_modules is not None:
|
|
for module in fix_modules:
|
|
for param in getattr(self, module).parameters():
|
|
param.requires_grad = False
|
|
|
|
self.connect_list = connect_list
|
|
self.n_layers = n_layers
|
|
self.dim_embd = dim_embd
|
|
self.dim_mlp = dim_embd*2
|
|
|
|
self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))
|
|
self.feat_emb = nn.Linear(256, self.dim_embd)
|
|
|
|
# transformer
|
|
self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0)
|
|
for _ in range(self.n_layers)])
|
|
|
|
# logits_predict head
|
|
self.idx_pred_layer = nn.Sequential(
|
|
nn.LayerNorm(dim_embd),
|
|
nn.Linear(dim_embd, codebook_size, bias=False))
|
|
|
|
self.channels = {
|
|
'16': 512,
|
|
'32': 256,
|
|
'64': 256,
|
|
'128': 128,
|
|
'256': 128,
|
|
'512': 64,
|
|
}
|
|
|
|
# after second residual block for > 16, before attn layer for ==16
|
|
self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18}
|
|
# after first residual block for > 16, before attn layer for ==16
|
|
self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21}
|
|
|
|
# fuse_convs_dict
|
|
self.fuse_convs_dict = nn.ModuleDict()
|
|
for f_size in self.connect_list:
|
|
in_ch = self.channels[f_size]
|
|
self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)
|
|
|
|
def _init_weights(self, module):
|
|
if isinstance(module, (nn.Linear, nn.Embedding)):
|
|
module.weight.data.normal_(mean=0.0, std=0.02)
|
|
if isinstance(module, nn.Linear) and module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
def forward(self, x, w=0, detach_16=True, code_only=False, adain=False):
|
|
# ################### Encoder #####################
|
|
enc_feat_dict = {}
|
|
out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
|
|
for i, block in enumerate(self.encoder.blocks):
|
|
x = block(x)
|
|
if i in out_list:
|
|
enc_feat_dict[str(x.shape[-1])] = x.clone()
|
|
|
|
lq_feat = x
|
|
# ################# Transformer ###################
|
|
# quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
|
|
pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1)
|
|
# BCHW -> BC(HW) -> (HW)BC
|
|
feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1))
|
|
query_emb = feat_emb
|
|
# Transformer encoder
|
|
for layer in self.ft_layers:
|
|
query_emb = layer(query_emb, query_pos=pos_emb)
|
|
|
|
# output logits
|
|
logits = self.idx_pred_layer(query_emb) # (hw)bn
|
|
logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n
|
|
|
|
if code_only: # for training stage II
|
|
# logits doesn't need softmax before cross_entropy loss
|
|
return logits, lq_feat
|
|
|
|
# ################# Quantization ###################
|
|
# if self.training:
|
|
# quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])
|
|
# # b(hw)c -> bc(hw) -> bchw
|
|
# quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)
|
|
# ------------
|
|
soft_one_hot = F.softmax(logits, dim=2)
|
|
_, top_idx = torch.topk(soft_one_hot, 1, dim=2)
|
|
quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256])
|
|
# preserve gradients
|
|
# quant_feat = lq_feat + (quant_feat - lq_feat).detach()
|
|
|
|
if detach_16:
|
|
quant_feat = quant_feat.detach() # for training stage III
|
|
if adain:
|
|
quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)
|
|
|
|
# ################## Generator ####################
|
|
x = quant_feat
|
|
fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
|
|
|
|
for i, block in enumerate(self.generator.blocks):
|
|
x = block(x)
|
|
if i in fuse_list: # fuse after i-th block
|
|
f_size = str(x.shape[-1])
|
|
if w>0:
|
|
x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w)
|
|
out = x
|
|
# logits doesn't need softmax before cross_entropy loss
|
|
return out, logits, lq_feat
|