Merge branch 'dev' into master

1 files changed, 11 insertions, 13 deletions

diff --git a/modules/codeformer/codeformer_arch.py b/modules/codeformer/codeformer_arch.py
index 11dcc3ee..12db6814 100644
--- a/modules/codeformer/codeformer_arch.py
+++ b/modules/codeformer/codeformer_arch.py
@@ -1,14 +1,12 @@
 # this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
 
 import math
-import numpy as np
 import torch
 from torch import nn, Tensor
 import torch.nn.functional as F
-from typing import Optional, List
+from typing import Optional
 
-from modules.codeformer.vqgan_arch import *
-from basicsr.utils import get_root_logger
+from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
 from basicsr.utils.registry import ARCH_REGISTRY
 
 def calc_mean_std(feat, eps=1e-5):
@@ -121,7 +119,7 @@ class TransformerSALayer(nn.Module):
                 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)
@@ -161,10 +159,10 @@ class Fuse_sft_block(nn.Module):
 
 @ARCH_REGISTRY.register()
 class CodeFormer(VQAutoEncoder):
-    def __init__(self, dim_embd=512, n_head=8, n_layers=9, 
+    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']):
+                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:
@@ -181,14 +179,14 @@ class CodeFormer(VQAutoEncoder):
         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) 
+        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,
@@ -223,7 +221,7 @@ class CodeFormer(VQAutoEncoder):
         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) 
+            x = block(x)
             if i in out_list:
                 enc_feat_dict[str(x.shape[-1])] = x.clone()
 
@@ -268,11 +266,11 @@ class CodeFormer(VQAutoEncoder):
         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) 
+            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
\ No newline at end of file
+        return out, logits, lq_feat