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import torch
from torch import nn
from torch.nn.utils import spectral_norm
from modeling.base import BaseNetwork
from layers.blocks import DestyleResBlock, Destyler, ResBlock
class IFRNet(BaseNetwork):
def __init__(self, base_n_channels, destyler_n_channels):
super(IFRNet, self).__init__()
self.destyler = Destyler(in_features=32768, num_features=destyler_n_channels) # from vgg features
self.ds_fc1 = nn.Linear(destyler_n_channels, base_n_channels * 2)
self.ds_res1 = DestyleResBlock(channels_in=3, channels_out=base_n_channels, kernel_size=5, stride=1, padding=2)
self.ds_fc2 = nn.Linear(destyler_n_channels, base_n_channels * 4)
self.ds_res2 = DestyleResBlock(channels_in=base_n_channels, channels_out=base_n_channels * 2, kernel_size=3, stride=2, padding=1)
self.ds_fc3 = nn.Linear(destyler_n_channels, base_n_channels * 4)
self.ds_res3 = DestyleResBlock(channels_in=base_n_channels * 2, channels_out=base_n_channels * 2, kernel_size=3, stride=1, padding=1)
self.ds_fc4 = nn.Linear(destyler_n_channels, base_n_channels * 8)
self.ds_res4 = DestyleResBlock(channels_in=base_n_channels * 2, channels_out=base_n_channels * 4, kernel_size=3, stride=2, padding=1)
self.ds_fc5 = nn.Linear(destyler_n_channels, base_n_channels * 8)
self.ds_res5 = DestyleResBlock(channels_in=base_n_channels * 4, channels_out=base_n_channels * 4, kernel_size=3, stride=1, padding=1)
self.ds_fc6 = nn.Linear(destyler_n_channels, base_n_channels * 16)
self.ds_res6 = DestyleResBlock(channels_in=base_n_channels * 4, channels_out=base_n_channels * 8, kernel_size=3, stride=2, padding=1)
self.upsample = nn.UpsamplingNearest2d(scale_factor=2.0)
self.res1 = ResBlock(channels_in=base_n_channels * 8, channels_out=base_n_channels * 4, kernel_size=3, stride=1, padding=1)
self.res2 = ResBlock(channels_in=base_n_channels * 4, channels_out=base_n_channels * 4, kernel_size=3, stride=1, padding=1)
self.res3 = ResBlock(channels_in=base_n_channels * 4, channels_out=base_n_channels * 2, kernel_size=3, stride=1, padding=1)
self.res4 = ResBlock(channels_in=base_n_channels * 2, channels_out=base_n_channels * 2, kernel_size=3, stride=1, padding=1)
self.res5 = ResBlock(channels_in=base_n_channels * 2, channels_out=base_n_channels, kernel_size=3, stride=1, padding=1)
self.conv1 = nn.Conv2d(base_n_channels, 3, kernel_size=3, stride=1, padding=1)
self.init_weights(init_type="normal", gain=0.02)
def forward(self, x, vgg_feat):
b_size, ch, h, w = vgg_feat.size()
vgg_feat = vgg_feat.view(b_size, ch * h * w)
vgg_feat = self.destyler(vgg_feat)
out = self.ds_res1(x, self.ds_fc1(vgg_feat))
out = self.ds_res2(out, self.ds_fc2(vgg_feat))
out = self.ds_res3(out, self.ds_fc3(vgg_feat))
out = self.ds_res4(out, self.ds_fc4(vgg_feat))
out = self.ds_res5(out, self.ds_fc5(vgg_feat))
aux = self.ds_res6(out, self.ds_fc6(vgg_feat))
out = self.upsample(aux)
out = self.res1(out)
out = self.res2(out)
out = self.upsample(out)
out = self.res3(out)
out = self.res4(out)
out = self.upsample(out)
out = self.res5(out)
out = self.conv1(out)
return out, aux
class CIFR_Encoder(IFRNet):
def __init__(self, base_n_channels, destyler_n_channels):
super(CIFR_Encoder, self).__init__(base_n_channels, destyler_n_channels)
def forward(self, x, vgg_feat):
b_size, ch, h, w = vgg_feat.size()
vgg_feat = vgg_feat.view(b_size, ch * h * w)
vgg_feat = self.destyler(vgg_feat)
feat1 = self.ds_res1(x, self.ds_fc1(vgg_feat))
feat2 = self.ds_res2(feat1, self.ds_fc2(vgg_feat))
feat3 = self.ds_res3(feat2, self.ds_fc3(vgg_feat))
feat4 = self.ds_res4(feat3, self.ds_fc4(vgg_feat))
feat5 = self.ds_res5(feat4, self.ds_fc5(vgg_feat))
feat6 = self.ds_res6(feat5, self.ds_fc6(vgg_feat))
feats = [feat1, feat2, feat3, feat4, feat5, feat6]
out = self.upsample(feat6)
out = self.res1(out)
out = self.res2(out)
out = self.upsample(out)
out = self.res3(out)
out = self.res4(out)
out = self.upsample(out)
out = self.res5(out)
out = self.conv1(out)
return out, feats
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1. / self.power)
out = x.div(norm + 1e-7)
return out
class PatchSampleF(BaseNetwork):
def __init__(self, base_n_channels, style_or_content, use_mlp=False, nc=256):
# potential issues: currently, we use the same patch_ids for multiple images in the batch
super(PatchSampleF, self).__init__()
self.is_content = True if style_or_content == "content" else False
self.l2norm = Normalize(2)
self.use_mlp = use_mlp
self.nc = nc # hard-coded
self.mlp_0 = nn.Sequential(*[nn.Linear(base_n_channels, self.nc), nn.ReLU(), nn.Linear(self.nc, self.nc)]).cuda()
self.mlp_1 = nn.Sequential(*[nn.Linear(base_n_channels * 2, self.nc), nn.ReLU(), nn.Linear(self.nc, self.nc)]).cuda()
self.mlp_2 = nn.Sequential(*[nn.Linear(base_n_channels * 2, self.nc), nn.ReLU(), nn.Linear(self.nc, self.nc)]).cuda()
self.mlp_3 = nn.Sequential(*[nn.Linear(base_n_channels * 4, self.nc), nn.ReLU(), nn.Linear(self.nc, self.nc)]).cuda()
self.mlp_4 = nn.Sequential(*[nn.Linear(base_n_channels * 4, self.nc), nn.ReLU(), nn.Linear(self.nc, self.nc)]).cuda()
self.mlp_5 = nn.Sequential(*[nn.Linear(base_n_channels * 8, self.nc), nn.ReLU(), nn.Linear(self.nc, self.nc)]).cuda()
self.init_weights(init_type="normal", gain=0.02)
@staticmethod
def gram_matrix(x):
# a, b, c, d = x.size() # a=batch size(=1)
a, b = x.size()
# b=number of feature maps
# (c,d)=dimensions of a f. map (N=c*d)
# features = x.view(a * b, c * d) # resise F_XL into \hat F_XL
G = torch.mm(x, x.t()) # compute the gram product
# we 'normalize' the values of the gram matrix
# by dividing by the number of element in each feature maps.
return G.div(a * b)
def forward(self, feats, num_patches=64, patch_ids=None):
return_ids = []
return_feats = []
for feat_id, feat in enumerate(feats):
B, C, H, W = feat.shape
feat_reshape = feat.permute(0, 2, 3, 1).flatten(1, 2)
if num_patches > 0:
if patch_ids is not None:
patch_id = patch_ids[feat_id]
else:
patch_id = torch.randperm(feat_reshape.shape[1], device=feats[0].device)
patch_id = patch_id[:int(min(num_patches, patch_id.shape[0]))] # .to(patch_ids.device)
x_sample = feat_reshape[:, patch_id, :].flatten(0, 1) # reshape(-1, x.shape[1])
else:
x_sample = feat_reshape
patch_id = []
if self.use_mlp:
mlp = getattr(self, 'mlp_%d' % feat_id)
x_sample = mlp(x_sample)
if not self.is_content:
x_sample = self.gram_matrix(x_sample)
return_ids.append(patch_id)
x_sample = self.l2norm(x_sample)
if num_patches == 0:
x_sample = x_sample.permute(0, 2, 1).reshape([B, x_sample.shape[-1], H, W])
return_feats.append(x_sample)
return return_feats, return_ids
class MLP(nn.Module):
def __init__(self, base_n_channels, out_features=14):
super(MLP, self).__init__()
self.aux_classifier = nn.Sequential(
nn.Conv2d(base_n_channels * 8, base_n_channels * 4, kernel_size=3, stride=1, padding=1),
nn.MaxPool2d(2),
nn.Conv2d(base_n_channels * 4, base_n_channels * 2, kernel_size=3, stride=1, padding=1),
nn.MaxPool2d(2),
# nn.Conv2d(base_n_channels * 2, base_n_channels * 1, kernel_size=3, stride=1, padding=1),
# nn.MaxPool2d(2),
Flatten(),
nn.Linear(base_n_channels * 8 * 8 * 2, out_features),
# nn.Softmax(dim=-1)
)
def forward(self, x):
return self.aux_classifier(x)
class Flatten(nn.Module):
def forward(self, input):
"""
Note that input.size(0) is usually the batch size.
So what it does is that given any input with input.size(0) # of batches,
will flatten to be 1 * nb_elements.
"""
batch_size = input.size(0)
out = input.view(batch_size, -1)
return out # (batch_size, *size)
class Discriminator(BaseNetwork):
def __init__(self, base_n_channels):
"""
img_size : (int, int, int)
Height and width must be powers of 2. E.g. (32, 32, 1) or
(64, 128, 3). Last number indicates number of channels, e.g. 1 for
grayscale or 3 for RGB
"""
super(Discriminator, self).__init__()
self.image_to_features = nn.Sequential(
spectral_norm(nn.Conv2d(3, base_n_channels, 5, 2, 2)),
nn.LeakyReLU(0.2, inplace=True),
spectral_norm(nn.Conv2d(base_n_channels, 2 * base_n_channels, 5, 2, 2)),
nn.LeakyReLU(0.2, inplace=True),
spectral_norm(nn.Conv2d(2 * base_n_channels, 2 * base_n_channels, 5, 2, 2)),
nn.LeakyReLU(0.2, inplace=True),
spectral_norm(nn.Conv2d(2 * base_n_channels, 4 * base_n_channels, 5, 2, 2)),
nn.LeakyReLU(0.2, inplace=True),
# spectral_norm(nn.Conv2d(4 * base_n_channels, 4 * base_n_channels, 5, 2, 2)),
# nn.LeakyReLU(0.2, inplace=True),
spectral_norm(nn.Conv2d(4 * base_n_channels, 8 * base_n_channels, 5, 1, 1)),
nn.LeakyReLU(0.2, inplace=True),
)
output_size = 8 * base_n_channels * 3 * 3
self.features_to_prob = nn.Sequential(
spectral_norm(nn.Conv2d(8 * base_n_channels, 2 * base_n_channels, 5, 2, 1)),
Flatten(),
nn.Linear(output_size, 1)
)
self.init_weights(init_type="normal", gain=0.02)
def forward(self, input_data):
x = self.image_to_features(input_data)
return self.features_to_prob(x)
class PatchDiscriminator(Discriminator):
def __init__(self, base_n_channels):
super(PatchDiscriminator, self).__init__(base_n_channels)
self.features_to_prob = nn.Sequential(
spectral_norm(nn.Conv2d(8 * base_n_channels, 1, 1)),
Flatten()
)
def forward(self, input_data):
x = self.image_to_features(input_data)
return self.features_to_prob(x)
if __name__ == '__main__':
import torchvision
ifrnet = CIFR_Encoder(32, 128).cuda()
x = torch.rand((2, 3, 256, 256)).cuda()
vgg16 = torchvision.models.vgg16(pretrained=True).features.eval().cuda()
with torch.no_grad():
vgg_feat = vgg16(x)
output, feats = ifrnet(x, vgg_feat)
print(output.size())
for i, feat in enumerate(feats):
print(i, feat.size())
disc = Discriminator(32).cuda()
d_out = disc(output)
print(d_out.size())
patch_disc = PatchDiscriminator(32).cuda()
p_d_out = patch_disc(output)
print(p_d_out.size())
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