File size: 9,281 Bytes
00e6746 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 |
import torch
import torch.nn as nn
from basicsr.archs.ddcolor_arch_utils.unet import Hook, CustomPixelShuffle_ICNR, UnetBlockWide, NormType, custom_conv_layer
from basicsr.archs.ddcolor_arch_utils.convnext import ConvNeXt
from basicsr.archs.ddcolor_arch_utils.transformer_utils import SelfAttentionLayer, CrossAttentionLayer, FFNLayer, MLP
from basicsr.archs.ddcolor_arch_utils.position_encoding import PositionEmbeddingSine
class DDColor(nn.Module):
def __init__(
self,
encoder_name='convnext-l',
decoder_name='MultiScaleColorDecoder',
num_input_channels=3,
input_size=(256, 256),
nf=512,
num_output_channels=3,
last_norm='Weight',
do_normalize=False,
num_queries=256,
num_scales=3,
dec_layers=9,
):
super().__init__()
self.encoder = ImageEncoder(encoder_name, ['norm0', 'norm1', 'norm2', 'norm3'])
self.encoder.eval()
test_input = torch.randn(1, num_input_channels, *input_size)
self.encoder(test_input)
self.decoder = DuelDecoder(
self.encoder.hooks,
nf=nf,
last_norm=last_norm,
num_queries=num_queries,
num_scales=num_scales,
dec_layers=dec_layers,
decoder_name=decoder_name
)
self.refine_net = nn.Sequential(
custom_conv_layer(num_queries + 3, num_output_channels, ks=1, use_activ=False, norm_type=NormType.Spectral)
)
self.do_normalize = do_normalize
self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
def normalize(self, img):
return (img - self.mean) / self.std
def denormalize(self, img):
return img * self.std + self.mean
def forward(self, x):
if x.shape[1] == 3:
x = self.normalize(x)
self.encoder(x)
out_feat = self.decoder()
coarse_input = torch.cat([out_feat, x], dim=1)
out = self.refine_net(coarse_input)
if self.do_normalize:
out = self.denormalize(out)
return out
class ImageEncoder(nn.Module):
def __init__(self, encoder_name, hook_names):
super().__init__()
assert encoder_name == 'convnext-t' or encoder_name == 'convnext-l'
if encoder_name == 'convnext-t':
self.arch = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768])
elif encoder_name == 'convnext-l':
self.arch = ConvNeXt(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536])
else:
raise NotImplementedError
self.encoder_name = encoder_name
self.hook_names = hook_names
self.hooks = self.setup_hooks()
def setup_hooks(self):
hooks = [Hook(self.arch._modules[name]) for name in self.hook_names]
return hooks
def forward(self, x):
return self.arch(x)
class DuelDecoder(nn.Module):
def __init__(
self,
hooks,
nf=512,
blur=True,
last_norm='Weight',
num_queries=256,
num_scales=3,
dec_layers=9,
decoder_name='MultiScaleColorDecoder',
):
super().__init__()
self.hooks = hooks
self.nf = nf
self.blur = blur
self.last_norm = getattr(NormType, last_norm)
self.decoder_name = decoder_name
self.layers = self.make_layers()
embed_dim = nf // 2
self.last_shuf = CustomPixelShuffle_ICNR(embed_dim, embed_dim, blur=self.blur, norm_type=self.last_norm, scale=4)
assert decoder_name == 'MultiScaleColorDecoder'
self.color_decoder = MultiScaleColorDecoder(
in_channels=[512, 512, 256],
num_queries=num_queries,
num_scales=num_scales,
dec_layers=dec_layers,
)
def make_layers(self):
decoder_layers = []
in_c = self.hooks[-1].feature.shape[1]
out_c = self.nf
setup_hooks = self.hooks[-2::-1]
for layer_index, hook in enumerate(setup_hooks):
feature_c = hook.feature.shape[1]
if layer_index == len(setup_hooks) - 1:
out_c = out_c // 2
decoder_layers.append(
UnetBlockWide(
in_c, feature_c, out_c, hook, blur=self.blur, self_attention=False, norm_type=NormType.Spectral))
in_c = out_c
return nn.Sequential(*decoder_layers)
def forward(self):
encode_feat = self.hooks[-1].feature
out0 = self.layers[0](encode_feat)
out1 = self.layers[1](out0)
out2 = self.layers[2](out1)
out3 = self.last_shuf(out2)
return self.color_decoder([out0, out1, out2], out3)
class MultiScaleColorDecoder(nn.Module):
def __init__(
self,
in_channels,
hidden_dim=256,
num_queries=100,
nheads=8,
dim_feedforward=2048,
dec_layers=9,
pre_norm=False,
color_embed_dim=256,
enforce_input_project=True,
num_scales=3,
):
super().__init__()
self.hidden_dim = hidden_dim
self.num_queries = num_queries
self.num_layers = dec_layers
self.num_feature_levels = num_scales
# Positional encoding layer
self.pe_layer = PositionEmbeddingSine(hidden_dim // 2, normalize=True)
# Learnable query features and embeddings
self.query_feat = nn.Embedding(num_queries, hidden_dim)
self.query_embed = nn.Embedding(num_queries, hidden_dim)
# Learnable level embeddings
self.level_embed = nn.Embedding(num_scales, hidden_dim)
# Input projection layers
self.input_proj = nn.ModuleList(
[self._make_input_proj(in_ch, hidden_dim, enforce_input_project) for in_ch in in_channels]
)
# Transformer layers
self.transformer_self_attention_layers = nn.ModuleList()
self.transformer_cross_attention_layers = nn.ModuleList()
self.transformer_ffn_layers = nn.ModuleList()
for _ in range(dec_layers):
self.transformer_self_attention_layers.append(
SelfAttentionLayer(
d_model=hidden_dim,
nhead=nheads,
dropout=0.0,
normalize_before=pre_norm,
)
)
self.transformer_cross_attention_layers.append(
CrossAttentionLayer(
d_model=hidden_dim,
nhead=nheads,
dropout=0.0,
normalize_before=pre_norm,
)
)
self.transformer_ffn_layers.append(
FFNLayer(
d_model=hidden_dim,
dim_feedforward=dim_feedforward,
dropout=0.0,
normalize_before=pre_norm,
)
)
# Layer normalization for the decoder output
self.decoder_norm = nn.LayerNorm(hidden_dim)
# Output embedding layer
self.color_embed = MLP(hidden_dim, hidden_dim, color_embed_dim, 3)
def forward(self, x, img_features):
assert len(x) == self.num_feature_levels
src, pos = self._get_src_and_pos(x)
bs = src[0].shape[1]
# Prepare query embeddings (QxNxC)
query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1)
output = self.query_feat.weight.unsqueeze(1).repeat(1, bs, 1)
for i in range(self.num_layers):
level_index = i % self.num_feature_levels
# attention: cross-attention first
output = self.transformer_cross_attention_layers[i](
output, src[level_index],
memory_mask=None,
memory_key_padding_mask=None,
pos=pos[level_index], query_pos=query_embed
)
output = self.transformer_self_attention_layers[i](
output, tgt_mask=None,
tgt_key_padding_mask=None,
query_pos=query_embed
)
# FFN
output = self.transformer_ffn_layers[i](
output
)
decoder_output = self.decoder_norm(output).transpose(0, 1)
color_embed = self.color_embed(decoder_output)
out = torch.einsum("bqc,bchw->bqhw", color_embed, img_features)
return out
def _make_input_proj(self, in_ch, hidden_dim, enforce):
if in_ch != hidden_dim or enforce:
proj = nn.Conv2d(in_ch, hidden_dim, kernel_size=1)
nn.init.kaiming_uniform_(proj.weight, a=1)
if proj.bias is not None:
nn.init.constant_(proj.bias, 0)
return proj
return nn.Sequential()
def _get_src_and_pos(self, x):
src, pos = [], []
for i, feature in enumerate(x):
pos.append(self.pe_layer(feature).flatten(2).permute(2, 0, 1)) # flatten NxCxHxW to HWxNxC
src.append((self.input_proj[i](feature).flatten(2) + self.level_embed.weight[i][None, :, None]).permute(2, 0, 1))
return src, pos
|