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