Spaces:
Runtime error
Runtime error
File size: 5,470 Bytes
1b2a9b1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import swapae.util as util
from .stylegan2_layers import Downsample
def gan_loss(pred, should_be_classified_as_real):
bs = pred.size(0)
if should_be_classified_as_real:
return F.softplus(-pred).view(bs, -1).mean(dim=1)
else:
return F.softplus(pred).view(bs, -1).mean(dim=1)
def feature_matching_loss(xs, ys, equal_weights=False, num_layers=6):
loss = 0.0
for i, (x, y) in enumerate(zip(xs[:num_layers], ys[:num_layers])):
if equal_weights:
weight = 1.0 / min(num_layers, len(xs))
else:
weight = 1 / (2 ** (min(num_layers, len(xs)) - i))
loss = loss + (x - y).abs().flatten(1).mean(1) * weight
return loss
class IntraImageNCELoss(nn.Module):
def __init__(self, opt):
super().__init__()
self.opt = opt
self.cross_entropy_loss = nn.CrossEntropyLoss(reduction='mean')
def forward(self, query, target):
num_locations = min(query.size(2) * query.size(3), self.opt.intraimage_num_locations)
bs = query.size(0)
patch_ids = torch.randperm(num_locations, device=query.device)
query = query.flatten(2, 3)
target = target.flatten(2, 3)
# both query and target are of size B x C x N
query = query[:, :, patch_ids]
target = target[:, :, patch_ids]
cosine_similarity = torch.bmm(query.transpose(1, 2), target)
cosine_similarity = cosine_similarity.flatten(0, 1)
target_label = torch.arange(num_locations, dtype=torch.long, device=query.device).repeat(bs)
loss = self.cross_entropy_loss(cosine_similarity / 0.07, target_label)
return loss
class VGG16Loss(torch.nn.Module):
def __init__(self):
super().__init__()
self.vgg_convs = torchvision.models.vgg16(pretrained=True).features
self.register_buffer('mean',
torch.tensor([0.485, 0.456, 0.406])[None, :, None, None] - 0.5)
self.register_buffer('stdev',
torch.tensor([0.229, 0.224, 0.225])[None, :, None, None] * 2)
self.downsample = Downsample([1, 2, 1], factor=2)
def copy_section(self, source, start, end):
slice = torch.nn.Sequential()
for i in range(start, end):
slice.add_module(str(i), source[i])
return slice
def vgg_forward(self, x):
x = (x - self.mean) / self.stdev
features = []
for name, layer in self.vgg_convs.named_children():
if "MaxPool2d" == type(layer).__name__:
features.append(x)
if len(features) == 3:
break
x = self.downsample(x)
else:
x = layer(x)
return features
def forward(self, x, y):
y = y.detach()
loss = 0
weights = [1 / 32, 1 / 16, 1 / 8, 1 / 4, 1.0]
#weights = [1] * 5
total_weights = 0.0
for i, (xf, yf) in enumerate(zip(self.vgg_forward(x), self.vgg_forward(y))):
loss += F.l1_loss(xf, yf) * weights[i]
total_weights += weights[i]
return loss / total_weights
class NCELoss(nn.Module):
def __init__(self):
super().__init__()
self.cross_entropy_loss = nn.CrossEntropyLoss(reduction='mean')
def forward(self, query, target, negatives):
query = util.normalize(query.flatten(1))
target = util.normalize(target.flatten(1))
negatives = util.normalize(negatives.flatten(1))
bs = query.size(0)
sim_pos = (query * target).sum(dim=1, keepdim=True)
sim_neg = torch.mm(query, negatives.transpose(0, 1))
all_similarity = torch.cat([sim_pos, sim_neg], axis=1) / 0.07
#sim_target = util.compute_similarity_logit(query, target)
#sim_target = torch.mm(query, target.transpose(0, 1)) / 0.07
#sim_query = util.compute_similarity_logit(query, query)
#util.set_diag_(sim_query, -20.0)
#all_similarity = torch.cat([sim_target, sim_query], axis=1)
#target_label = torch.arange(bs, dtype=torch.long,
# device=query.device)
target_label = torch.zeros(bs, dtype=torch.long, device=query.device)
loss = self.cross_entropy_loss(all_similarity,
target_label)
return loss
class ScaleInvariantReconstructionLoss(nn.Module):
def forward(self, query, target):
query_flat = query.transpose(1, 3)
target_flat = target.transpose(1, 3)
dist = 1.0 - torch.bmm(
query_flat[:, :, :, None, :].flatten(0, 2),
target_flat[:, :, :, :, None].flatten(0, 2),
)
target_spatially_flat = target.flatten(1, 2)
num_samples = min(target_spatially_flat.size(1), 64)
random_indices = torch.randperm(num_samples, dtype=torch.long, device=target.device)
randomly_sampled = target_spatially_flat[:, random_indices]
random_indices = torch.randperm(num_samples, dtype=torch.long, device=target.device)
another_random_sample = target_spatially_flat[:, random_indices]
random_similarity = torch.bmm(
randomly_sampled[:, :, None, :].flatten(0, 1),
torch.flip(another_random_sample, [0])[:, :, :, None].flatten(0, 1)
)
return dist.mean() + random_similarity.clamp(min=0.0).mean()
|