# coding=utf-8
# Copyright 2019 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
class ConditionalInstanceNorm2dPlus(nn.Module):
def __init__(self, num_features, num_classes, bias=True):
super().__init__()
self.num_features = num_features
self.bias = bias
self.instance_norm = nn.InstanceNorm2d(num_features, affine=False, track_running_stats=False)
if bias:
self.embed = nn.Embedding(num_classes, num_features * 3)
self.embed.weight.data[:, :2 * num_features].normal_(1, 0.02) # Initialise scale at N(1, 0.02)
self.embed.weight.data[:, 2 * num_features:].zero_() # Initialise bias at 0
else:
self.embed = nn.Embedding(num_classes, 2 * num_features)
self.embed.weight.data.normal_(1, 0.02)
def forward(self, x, y):
means = torch.mean(x, dim=(2, 3))
m = torch.mean(means, dim=-1, keepdim=True)
v = torch.var(means, dim=-1, keepdim=True)
means = (means - m) / (torch.sqrt(v + 1e-5))
h = self.instance_norm(x)
if self.bias:
gamma, alpha, beta = self.embed(y).chunk(3, dim=-1)
h = h + means[..., None, None] * alpha[..., None, None]
out = gamma.view(-1, self.num_features, 1, 1) * h + beta.view(-1, self.num_features, 1, 1)
else:
gamma, alpha = self.embed(y).chunk(2, dim=-1)
h = h + means[..., None, None] * alpha[..., None, None]
out = gamma.view(-1, self.num_features, 1, 1) * h
return out
class ConditionalActNorm(nn.Module):
def __init__(self, num_features, num_classes):
super().__init__()
self.num_features = num_features
self.num_classes = num_classes
self.embed = nn.Embedding(num_classes, num_features * 2)
self.embed.weight.data.zero_()
self.init = False
def forward(self, x, y):
if self.init:
scale, bias = self.embed(y).chunk(2, dim=-1)
return x * scale[:, :, None, None] + bias[:, :, None, None]
else:
m, v = torch.mean(x, dim=(0, 2, 3)), torch.var(x, dim=(0, 2, 3))
std = torch.sqrt(v + 1e-5)
scale_init = 1. / std
bias_init = -1. * m / std
self.embed.weight.data[:, :self.num_features] = scale_init[None].repeat(self.num_classes, 1)
self.embed.weight.data[:, self.num_features:] = bias_init[None].repeat(self.num_classes, 1)
self.init = True
return self(x, y)
logabs = lambda x: torch.log(torch.abs(x))
class ActNorm(nn.Module):
def __init__(self, in_channel, logdet=True):
super().__init__()
self.loc = nn.Parameter(torch.zeros(1, in_channel, 1, 1))
self.scale = nn.Parameter(torch.ones(1, in_channel, 1, 1))
self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8))
self.logdet = logdet
def initialize(self, input):
with torch.no_grad():
flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
mean = (
flatten.mean(1)
.unsqueeze(1)
.unsqueeze(2)
.unsqueeze(3)
.permute(1, 0, 2, 3)
)
std = (
flatten.std(1)
.unsqueeze(1)
.unsqueeze(2)
.unsqueeze(3)
.permute(1, 0, 2, 3)
)
self.loc.data.copy_(-mean)
self.scale.data.copy_(1 / (std + 1e-6))
def forward(self, input):
_, _, height, width = input.shape
if self.initialized.item() == 0:
self.initialize(input)
self.initialized.fill_(1)
log_abs = logabs(self.scale)
logdet = height * width * torch.sum(log_abs)
if self.logdet:
return self.scale * (input + self.loc), logdet
else:
return self.scale * (input + self.loc)
def reverse(self, output):
return output / self.scale - self.loc
class ContinuousConditionalActNorm(nn.Module):
def __init__(self, num_features, num_classes):
super().__init__()
del num_classes
self.num_features = num_features
self.embed = nn.Sequential(nn.Linear(1, 256),
nn.ELU(inplace=True),
nn.Linear(256, 256),
nn.ELU(inplace=True),
nn.Linear(256, self.num_features*2),
)
def forward(self, x, y):
scale, bias = self.embed(y.unsqueeze(-1)).chunk(2, dim=-1)
return x * scale[:, :, None, None] + bias[:, :, None, None]
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, *args, **kwargs):
return
