File size: 10,729 Bytes
18dd6ad |
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 |
from typing import List, Tuple, Union, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from annotator.lama.saicinpainting.training.modules.base import get_conv_block_ctor, get_activation
from annotator.lama.saicinpainting.training.modules.pix2pixhd import ResnetBlock
class ResNetHead(nn.Module):
def __init__(self, input_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
padding_type='reflect', conv_kind='default', activation=nn.ReLU(True)):
assert (n_blocks >= 0)
super(ResNetHead, self).__init__()
conv_layer = get_conv_block_ctor(conv_kind)
model = [nn.ReflectionPad2d(3),
conv_layer(input_nc, ngf, kernel_size=7, padding=0),
norm_layer(ngf),
activation]
### downsample
for i in range(n_downsampling):
mult = 2 ** i
model += [conv_layer(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
norm_layer(ngf * mult * 2),
activation]
mult = 2 ** n_downsampling
### resnet blocks
for i in range(n_blocks):
model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
conv_kind=conv_kind)]
self.model = nn.Sequential(*model)
def forward(self, input):
return self.model(input)
class ResNetTail(nn.Module):
def __init__(self, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0,
add_in_proj=None):
assert (n_blocks >= 0)
super(ResNetTail, self).__init__()
mult = 2 ** n_downsampling
model = []
if add_in_proj is not None:
model.append(nn.Conv2d(add_in_proj, ngf * mult, kernel_size=1))
### resnet blocks
for i in range(n_blocks):
model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
conv_kind=conv_kind)]
### upsample
for i in range(n_downsampling):
mult = 2 ** (n_downsampling - i)
model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,
output_padding=1),
up_norm_layer(int(ngf * mult / 2)),
up_activation]
self.model = nn.Sequential(*model)
out_layers = []
for _ in range(out_extra_layers_n):
out_layers += [nn.Conv2d(ngf, ngf, kernel_size=1, padding=0),
up_norm_layer(ngf),
up_activation]
out_layers += [nn.ReflectionPad2d(3),
nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
if add_out_act:
out_layers.append(get_activation('tanh' if add_out_act is True else add_out_act))
self.out_proj = nn.Sequential(*out_layers)
def forward(self, input, return_last_act=False):
features = self.model(input)
out = self.out_proj(features)
if return_last_act:
return out, features
else:
return out
class MultiscaleResNet(nn.Module):
def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=2, n_blocks_head=2, n_blocks_tail=6, n_scales=3,
norm_layer=nn.BatchNorm2d, padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0,
out_cumulative=False, return_only_hr=False):
super().__init__()
self.heads = nn.ModuleList([ResNetHead(input_nc, ngf=ngf, n_downsampling=n_downsampling,
n_blocks=n_blocks_head, norm_layer=norm_layer, padding_type=padding_type,
conv_kind=conv_kind, activation=activation)
for i in range(n_scales)])
tail_in_feats = ngf * (2 ** n_downsampling) + ngf
self.tails = nn.ModuleList([ResNetTail(output_nc,
ngf=ngf, n_downsampling=n_downsampling,
n_blocks=n_blocks_tail, norm_layer=norm_layer, padding_type=padding_type,
conv_kind=conv_kind, activation=activation, up_norm_layer=up_norm_layer,
up_activation=up_activation, add_out_act=add_out_act,
out_extra_layers_n=out_extra_layers_n,
add_in_proj=None if (i == n_scales - 1) else tail_in_feats)
for i in range(n_scales)])
self.out_cumulative = out_cumulative
self.return_only_hr = return_only_hr
@property
def num_scales(self):
return len(self.heads)
def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \
-> Union[torch.Tensor, List[torch.Tensor]]:
"""
:param ms_inputs: List of inputs of different resolutions from HR to LR
:param smallest_scales_num: int or None, number of smallest scales to take at input
:return: Depending on return_only_hr:
True: Only the most HR output
False: List of outputs of different resolutions from HR to LR
"""
if smallest_scales_num is None:
assert len(self.heads) == len(ms_inputs), (len(self.heads), len(ms_inputs), smallest_scales_num)
smallest_scales_num = len(self.heads)
else:
assert smallest_scales_num == len(ms_inputs) <= len(self.heads), (len(self.heads), len(ms_inputs), smallest_scales_num)
cur_heads = self.heads[-smallest_scales_num:]
ms_features = [cur_head(cur_inp) for cur_head, cur_inp in zip(cur_heads, ms_inputs)]
all_outputs = []
prev_tail_features = None
for i in range(len(ms_features)):
scale_i = -i - 1
cur_tail_input = ms_features[-i - 1]
if prev_tail_features is not None:
if prev_tail_features.shape != cur_tail_input.shape:
prev_tail_features = F.interpolate(prev_tail_features, size=cur_tail_input.shape[2:],
mode='bilinear', align_corners=False)
cur_tail_input = torch.cat((cur_tail_input, prev_tail_features), dim=1)
cur_out, cur_tail_feats = self.tails[scale_i](cur_tail_input, return_last_act=True)
prev_tail_features = cur_tail_feats
all_outputs.append(cur_out)
if self.out_cumulative:
all_outputs_cum = [all_outputs[0]]
for i in range(1, len(ms_features)):
cur_out = all_outputs[i]
cur_out_cum = cur_out + F.interpolate(all_outputs_cum[-1], size=cur_out.shape[2:],
mode='bilinear', align_corners=False)
all_outputs_cum.append(cur_out_cum)
all_outputs = all_outputs_cum
if self.return_only_hr:
return all_outputs[-1]
else:
return all_outputs[::-1]
class MultiscaleDiscriminatorSimple(nn.Module):
def __init__(self, ms_impl):
super().__init__()
self.ms_impl = nn.ModuleList(ms_impl)
@property
def num_scales(self):
return len(self.ms_impl)
def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \
-> List[Tuple[torch.Tensor, List[torch.Tensor]]]:
"""
:param ms_inputs: List of inputs of different resolutions from HR to LR
:param smallest_scales_num: int or None, number of smallest scales to take at input
:return: List of pairs (prediction, features) for different resolutions from HR to LR
"""
if smallest_scales_num is None:
assert len(self.ms_impl) == len(ms_inputs), (len(self.ms_impl), len(ms_inputs), smallest_scales_num)
smallest_scales_num = len(self.heads)
else:
assert smallest_scales_num == len(ms_inputs) <= len(self.ms_impl), \
(len(self.ms_impl), len(ms_inputs), smallest_scales_num)
return [cur_discr(cur_input) for cur_discr, cur_input in zip(self.ms_impl[-smallest_scales_num:], ms_inputs)]
class SingleToMultiScaleInputMixin:
def forward(self, x: torch.Tensor) -> List:
orig_height, orig_width = x.shape[2:]
factors = [2 ** i for i in range(self.num_scales)]
ms_inputs = [F.interpolate(x, size=(orig_height // f, orig_width // f), mode='bilinear', align_corners=False)
for f in factors]
return super().forward(ms_inputs)
class GeneratorMultiToSingleOutputMixin:
def forward(self, x):
return super().forward(x)[0]
class DiscriminatorMultiToSingleOutputMixin:
def forward(self, x):
out_feat_tuples = super().forward(x)
return out_feat_tuples[0][0], [f for _, flist in out_feat_tuples for f in flist]
class DiscriminatorMultiToSingleOutputStackedMixin:
def __init__(self, *args, return_feats_only_levels=None, **kwargs):
super().__init__(*args, **kwargs)
self.return_feats_only_levels = return_feats_only_levels
def forward(self, x):
out_feat_tuples = super().forward(x)
outs = [out for out, _ in out_feat_tuples]
scaled_outs = [outs[0]] + [F.interpolate(cur_out, size=outs[0].shape[-2:],
mode='bilinear', align_corners=False)
for cur_out in outs[1:]]
out = torch.cat(scaled_outs, dim=1)
if self.return_feats_only_levels is not None:
feat_lists = [out_feat_tuples[i][1] for i in self.return_feats_only_levels]
else:
feat_lists = [flist for _, flist in out_feat_tuples]
feats = [f for flist in feat_lists for f in flist]
return out, feats
class MultiscaleDiscrSingleInput(SingleToMultiScaleInputMixin, DiscriminatorMultiToSingleOutputStackedMixin, MultiscaleDiscriminatorSimple):
pass
class MultiscaleResNetSingle(GeneratorMultiToSingleOutputMixin, SingleToMultiScaleInputMixin, MultiscaleResNet):
pass
|