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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
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