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96 lines
3.1 KiB
96 lines
3.1 KiB
from torch import nn
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import torch.nn.functional as F
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from modules.util import kp2gaussian
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import torch
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class DownBlock2d(nn.Module):
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"""
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Simple block for processing video (encoder).
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"""
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def __init__(self, in_features, out_features, norm=False, kernel_size=4, pool=False, sn=False):
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super(DownBlock2d, self).__init__()
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self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size)
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if sn:
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self.conv = nn.utils.spectral_norm(self.conv)
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if norm:
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self.norm = nn.InstanceNorm2d(out_features, affine=True)
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else:
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self.norm = None
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self.pool = pool
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def forward(self, x):
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out = x
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out = self.conv(out)
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if self.norm:
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out = self.norm(out)
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out = F.leaky_relu(out, 0.2)
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if self.pool:
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out = F.avg_pool2d(out, (2, 2))
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return out
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class Discriminator(nn.Module):
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"""
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Discriminator similar to Pix2Pix
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"""
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def __init__(self, num_channels=3, block_expansion=64, num_blocks=4, max_features=512,
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sn=False, use_kp=False, num_kp=10, kp_variance=0.01, **kwargs):
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super(Discriminator, self).__init__()
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down_blocks = []
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for i in range(num_blocks):
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down_blocks.append(
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DownBlock2d(num_channels + num_kp * use_kp if i == 0 else min(max_features, block_expansion * (2 ** i)),
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min(max_features, block_expansion * (2 ** (i + 1))),
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norm=(i != 0), kernel_size=4, pool=(i != num_blocks - 1), sn=sn))
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self.down_blocks = nn.ModuleList(down_blocks)
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self.conv = nn.Conv2d(self.down_blocks[-1].conv.out_channels, out_channels=1, kernel_size=1)
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if sn:
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self.conv = nn.utils.spectral_norm(self.conv)
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self.use_kp = use_kp
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self.kp_variance = kp_variance
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def forward(self, x, kp=None):
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feature_maps = []
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out = x
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if self.use_kp:
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heatmap = kp2gaussian(kp, x.shape[2:], self.kp_variance)
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out = torch.cat([out, heatmap], dim=1)
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for down_block in self.down_blocks:
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feature_maps.append(down_block(out))
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out = feature_maps[-1]
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prediction_map = self.conv(out)
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return feature_maps, prediction_map
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class MultiScaleDiscriminator(nn.Module):
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"""
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Multi-scale (scale) discriminator
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"""
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def __init__(self, scales=(), **kwargs):
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super(MultiScaleDiscriminator, self).__init__()
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self.scales = scales
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discs = {}
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for scale in scales:
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discs[str(scale).replace('.', '-')] = Discriminator(**kwargs)
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self.discs = nn.ModuleDict(discs)
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def forward(self, x, kp=None):
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out_dict = {}
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for scale, disc in self.discs.items():
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scale = str(scale).replace('-', '.')
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key = 'prediction_' + scale
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feature_maps, prediction_map = disc(x[key], kp)
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out_dict['feature_maps_' + scale] = feature_maps
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out_dict['prediction_map_' + scale] = prediction_map
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return out_dict
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