from torch import nn import torch.nn.functional as F import torch from sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d from sync_batchnorm import SynchronizedBatchNorm3d as BatchNorm3d import torch.nn.utils.spectral_norm as spectral_norm import re def kp2gaussian(kp, spatial_size, kp_variance): """ Transform a keypoint into gaussian like representation """ mean = kp['value'] coordinate_grid = make_coordinate_grid(spatial_size, mean.type()) number_of_leading_dimensions = len(mean.shape) - 1 shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape coordinate_grid = coordinate_grid.view(*shape) repeats = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 1) coordinate_grid = coordinate_grid.repeat(*repeats) # Preprocess kp shape shape = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 3) mean = mean.view(*shape) mean_sub = (coordinate_grid - mean) out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance) return out def make_coordinate_grid_2d(spatial_size, type): """ Create a meshgrid [-1,1] x [-1,1] of given spatial_size. """ h, w = spatial_size x = torch.arange(w).type(type) y = torch.arange(h).type(type) x = (2 * (x / (w - 1)) - 1) y = (2 * (y / (h - 1)) - 1) yy = y.view(-1, 1).repeat(1, w) xx = x.view(1, -1).repeat(h, 1) meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2) return meshed def make_coordinate_grid(spatial_size, type): d, h, w = spatial_size x = torch.arange(w).type(type) y = torch.arange(h).type(type) z = torch.arange(d).type(type) x = (2 * (x / (w - 1)) - 1) y = (2 * (y / (h - 1)) - 1) z = (2 * (z / (d - 1)) - 1) yy = y.view(1, -1, 1).repeat(d, 1, w) xx = x.view(1, 1, -1).repeat(d, h, 1) zz = z.view(-1, 1, 1).repeat(1, h, w) meshed = torch.cat([xx.unsqueeze_(3), yy.unsqueeze_(3), zz.unsqueeze_(3)], 3) return meshed class ResBottleneck(nn.Module): def __init__(self, in_features, stride): super(ResBottleneck, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features//4, kernel_size=1) self.conv2 = nn.Conv2d(in_channels=in_features//4, out_channels=in_features//4, kernel_size=3, padding=1, stride=stride) self.conv3 = nn.Conv2d(in_channels=in_features//4, out_channels=in_features, kernel_size=1) self.norm1 = BatchNorm2d(in_features//4, affine=True) self.norm2 = BatchNorm2d(in_features//4, affine=True) self.norm3 = BatchNorm2d(in_features, affine=True) self.stride = stride if self.stride != 1: self.skip = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=1, stride=stride) self.norm4 = BatchNorm2d(in_features, affine=True) def forward(self, x): out = self.conv1(x) out = self.norm1(out) out = F.relu(out) out = self.conv2(out) out = self.norm2(out) out = F.relu(out) out = self.conv3(out) out = self.norm3(out) if self.stride != 1: x = self.skip(x) x = self.norm4(x) out += x out = F.relu(out) return out class ResBlock2d(nn.Module): """ Res block, preserve spatial resolution. """ def __init__(self, in_features, kernel_size, padding): super(ResBlock2d, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, padding=padding) self.conv2 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, padding=padding) self.norm1 = BatchNorm2d(in_features, affine=True) self.norm2 = BatchNorm2d(in_features, affine=True) def forward(self, x): out = self.norm1(x) out = F.relu(out) out = self.conv1(out) out = self.norm2(out) out = F.relu(out) out = self.conv2(out) out += x return out class ResBlock3d(nn.Module): """ Res block, preserve spatial resolution. """ def __init__(self, in_features, kernel_size, padding): super(ResBlock3d, self).__init__() self.conv1 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, padding=padding) self.conv2 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, padding=padding) self.norm1 = BatchNorm3d(in_features, affine=True) self.norm2 = BatchNorm3d(in_features, affine=True) def forward(self, x): out = self.norm1(x) out = F.relu(out) out = self.conv1(out) out = self.norm2(out) out = F.relu(out) out = self.conv2(out) out += x return out class UpBlock2d(nn.Module): """ Upsampling block for use in decoder. """ def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): super(UpBlock2d, self).__init__() self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, padding=padding, groups=groups) self.norm = BatchNorm2d(out_features, affine=True) def forward(self, x): out = F.interpolate(x, scale_factor=2) out = self.conv(out) out = self.norm(out) out = F.relu(out) return out class UpBlock3d(nn.Module): """ Upsampling block for use in decoder. """ def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): super(UpBlock3d, self).__init__() self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, padding=padding, groups=groups) self.norm = BatchNorm3d(out_features, affine=True) def forward(self, x): # out = F.interpolate(x, scale_factor=(1, 2, 2), mode='trilinear') out = F.interpolate(x, scale_factor=(1, 2, 2)) out = self.conv(out) out = self.norm(out) out = F.relu(out) return out class DownBlock2d(nn.Module): """ Downsampling block for use in encoder. """ def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): super(DownBlock2d, self).__init__() self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, padding=padding, groups=groups) self.norm = BatchNorm2d(out_features, affine=True) self.pool = nn.AvgPool2d(kernel_size=(2, 2)) def forward(self, x): out = self.conv(x) out = self.norm(out) out = F.relu(out) out = self.pool(out) return out class DownBlock3d(nn.Module): """ Downsampling block for use in encoder. """ def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): super(DownBlock3d, self).__init__() ''' self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, padding=padding, groups=groups, stride=(1, 2, 2)) ''' self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, padding=padding, groups=groups) self.norm = BatchNorm3d(out_features, affine=True) self.pool = nn.AvgPool3d(kernel_size=(1, 2, 2)) def forward(self, x): out = self.conv(x) out = self.norm(out) out = F.relu(out) out = self.pool(out) return out class SameBlock2d(nn.Module): """ Simple block, preserve spatial resolution. """ def __init__(self, in_features, out_features, groups=1, kernel_size=3, padding=1, lrelu=False): super(SameBlock2d, self).__init__() self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, padding=padding, groups=groups) self.norm = BatchNorm2d(out_features, affine=True) if lrelu: self.ac = nn.LeakyReLU() else: self.ac = nn.ReLU() def forward(self, x): out = self.conv(x) out = self.norm(out) out = self.ac(out) return out class Encoder(nn.Module): """ Hourglass Encoder """ def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): super(Encoder, self).__init__() down_blocks = [] for i in range(num_blocks): down_blocks.append(DownBlock3d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)), min(max_features, block_expansion * (2 ** (i + 1))), kernel_size=3, padding=1)) self.down_blocks = nn.ModuleList(down_blocks) def forward(self, x): outs = [x] for down_block in self.down_blocks: outs.append(down_block(outs[-1])) return outs class Decoder(nn.Module): """ Hourglass Decoder """ def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): super(Decoder, self).__init__() up_blocks = [] for i in range(num_blocks)[::-1]: in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1))) out_filters = min(max_features, block_expansion * (2 ** i)) up_blocks.append(UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1)) self.up_blocks = nn.ModuleList(up_blocks) # self.out_filters = block_expansion self.out_filters = block_expansion + in_features self.conv = nn.Conv3d(in_channels=self.out_filters, out_channels=self.out_filters, kernel_size=3, padding=1) self.norm = BatchNorm3d(self.out_filters, affine=True) def forward(self, x): out = x.pop() # for up_block in self.up_blocks[:-1]: for up_block in self.up_blocks: out = up_block(out) skip = x.pop() out = torch.cat([out, skip], dim=1) # out = self.up_blocks[-1](out) out = self.conv(out) out = self.norm(out) out = F.relu(out) return out class Hourglass(nn.Module): """ Hourglass architecture. """ def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): super(Hourglass, self).__init__() self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features) self.decoder = Decoder(block_expansion, in_features, num_blocks, max_features) self.out_filters = self.decoder.out_filters def forward(self, x): return self.decoder(self.encoder(x)) class KPHourglass(nn.Module): """ Hourglass architecture. """ def __init__(self, block_expansion, in_features, reshape_features, reshape_depth, num_blocks=3, max_features=256): super(KPHourglass, self).__init__() self.down_blocks = nn.Sequential() for i in range(num_blocks): self.down_blocks.add_module('down'+ str(i), DownBlock2d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)), min(max_features, block_expansion * (2 ** (i + 1))), kernel_size=3, padding=1)) in_filters = min(max_features, block_expansion * (2 ** num_blocks)) self.conv = nn.Conv2d(in_channels=in_filters, out_channels=reshape_features, kernel_size=1) self.up_blocks = nn.Sequential() for i in range(num_blocks): in_filters = min(max_features, block_expansion * (2 ** (num_blocks - i))) out_filters = min(max_features, block_expansion * (2 ** (num_blocks - i - 1))) self.up_blocks.add_module('up'+ str(i), UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1)) self.reshape_depth = reshape_depth self.out_filters = out_filters def forward(self, x): out = self.down_blocks(x) out = self.conv(out) bs, c, h, w = out.shape out = out.view(bs, c//self.reshape_depth, self.reshape_depth, h, w) out = self.up_blocks(out) return out class AntiAliasInterpolation2d(nn.Module): """ Band-limited downsampling, for better preservation of the input signal. """ def __init__(self, channels, scale): super(AntiAliasInterpolation2d, self).__init__() sigma = (1 / scale - 1) / 2 kernel_size = 2 * round(sigma * 4) + 1 self.ka = kernel_size // 2 self.kb = self.ka - 1 if kernel_size % 2 == 0 else self.ka kernel_size = [kernel_size, kernel_size] sigma = [sigma, sigma] # The gaussian kernel is the product of the # gaussian function of each dimension. kernel = 1 meshgrids = torch.meshgrid( [ torch.arange(size, dtype=torch.float32) for size in kernel_size ] ) for size, std, mgrid in zip(kernel_size, sigma, meshgrids): mean = (size - 1) / 2 kernel *= torch.exp(-(mgrid - mean) ** 2 / (2 * std ** 2)) # Make sure sum of values in gaussian kernel equals 1. kernel = kernel / torch.sum(kernel) # Reshape to depthwise convolutional weight kernel = kernel.view(1, 1, *kernel.size()) kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1)) self.register_buffer('weight', kernel) self.groups = channels self.scale = scale inv_scale = 1 / scale self.int_inv_scale = int(inv_scale) def forward(self, input): if self.scale == 1.0: return input out = F.pad(input, (self.ka, self.kb, self.ka, self.kb)) out = F.conv2d(out, weight=self.weight, groups=self.groups) out = out[:, :, ::self.int_inv_scale, ::self.int_inv_scale] return out class SPADE(nn.Module): def __init__(self, norm_nc, label_nc): super().__init__() self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False) nhidden = 128 self.mlp_shared = nn.Sequential( nn.Conv2d(label_nc, nhidden, kernel_size=3, padding=1), nn.ReLU()) self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1) self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1) def forward(self, x, segmap): normalized = self.param_free_norm(x) segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest') actv = self.mlp_shared(segmap) gamma = self.mlp_gamma(actv) beta = self.mlp_beta(actv) out = normalized * (1 + gamma) + beta return out class SPADEResnetBlock(nn.Module): def __init__(self, fin, fout, norm_G, label_nc, use_se=False, dilation=1): super().__init__() # Attributes self.learned_shortcut = (fin != fout) fmiddle = min(fin, fout) self.use_se = use_se # create conv layers self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=dilation, dilation=dilation) self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=dilation, dilation=dilation) if self.learned_shortcut: self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False) # apply spectral norm if specified if 'spectral' in norm_G: self.conv_0 = spectral_norm(self.conv_0) self.conv_1 = spectral_norm(self.conv_1) if self.learned_shortcut: self.conv_s = spectral_norm(self.conv_s) # define normalization layers self.norm_0 = SPADE(fin, label_nc) self.norm_1 = SPADE(fmiddle, label_nc) if self.learned_shortcut: self.norm_s = SPADE(fin, label_nc) def forward(self, x, seg1): x_s = self.shortcut(x, seg1) dx = self.conv_0(self.actvn(self.norm_0(x, seg1))) dx = self.conv_1(self.actvn(self.norm_1(dx, seg1))) out = x_s + dx return out def shortcut(self, x, seg1): if self.learned_shortcut: x_s = self.conv_s(self.norm_s(x, seg1)) else: x_s = x return x_s def actvn(self, x): return F.leaky_relu(x, 2e-1)