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