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'''
Copyright (C) 2019 NVIDIA Corporation. Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu.
BSD License. All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE.
IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL
DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING
OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
'''
import torch
import torch.nn as nn
import functools
import numpy as np
import pytorch_lightning as pl
###############################################################################
# Functions
###############################################################################
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm2d') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
def get_norm_layer(norm_type='instance'):
if norm_type == 'batch':
norm_layer = functools.partial(nn.BatchNorm2d, affine=True)
elif norm_type == 'instance':
norm_layer = functools.partial(nn.InstanceNorm2d, affine=False)
else:
raise NotImplementedError('normalization layer [%s] is not found' %
norm_type)
return norm_layer
def define_G(input_nc,
output_nc,
ngf,
netG,
n_downsample_global=3,
n_blocks_global=9,
n_local_enhancers=1,
n_blocks_local=3,
norm='instance',
gpu_ids=[],
last_op=nn.Tanh()):
norm_layer = get_norm_layer(norm_type=norm)
if netG == 'global':
netG = GlobalGenerator(input_nc,
output_nc,
ngf,
n_downsample_global,
n_blocks_global,
norm_layer,
last_op=last_op)
elif netG == 'local':
netG = LocalEnhancer(input_nc, output_nc, ngf, n_downsample_global,
n_blocks_global, n_local_enhancers,
n_blocks_local, norm_layer)
elif netG == 'encoder':
netG = Encoder(input_nc, output_nc, ngf, n_downsample_global,
norm_layer)
else:
raise ('generator not implemented!')
# print(netG)
if len(gpu_ids) > 0:
assert (torch.cuda.is_available())
device=torch.device(f"cuda:{gpu_ids[0]}")
netG = netG.to(device)
netG.apply(weights_init)
return netG
def print_network(net):
if isinstance(net, list):
net = net[0]
num_params = 0
for param in net.parameters():
num_params += param.numel()
print(net)
print('Total number of parameters: %d' % num_params)
##############################################################################
# Generator
##############################################################################
class LocalEnhancer(pl.LightningModule):
def __init__(self,
input_nc,
output_nc,
ngf=32,
n_downsample_global=3,
n_blocks_global=9,
n_local_enhancers=1,
n_blocks_local=3,
norm_layer=nn.BatchNorm2d,
padding_type='reflect'):
super(LocalEnhancer, self).__init__()
self.n_local_enhancers = n_local_enhancers
###### global generator model #####
ngf_global = ngf * (2**n_local_enhancers)
model_global = GlobalGenerator(input_nc, output_nc, ngf_global,
n_downsample_global, n_blocks_global,
norm_layer).model
model_global = [model_global[i] for i in range(len(model_global) - 3)
] # get rid of final convolution layers
self.model = nn.Sequential(*model_global)
###### local enhancer layers #####
for n in range(1, n_local_enhancers + 1):
# downsample
ngf_global = ngf * (2**(n_local_enhancers - n))
model_downsample = [
nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, ngf_global, kernel_size=7, padding=0),
norm_layer(ngf_global),
nn.ReLU(True),
nn.Conv2d(ngf_global,
ngf_global * 2,
kernel_size=3,
stride=2,
padding=1),
norm_layer(ngf_global * 2),
nn.ReLU(True)
]
# residual blocks
model_upsample = []
for i in range(n_blocks_local):
model_upsample += [
ResnetBlock(ngf_global * 2,
padding_type=padding_type,
norm_layer=norm_layer)
]
# upsample
model_upsample += [
nn.ConvTranspose2d(ngf_global * 2,
ngf_global,
kernel_size=3,
stride=2,
padding=1,
output_padding=1),
norm_layer(ngf_global),
nn.ReLU(True)
]
# final convolution
if n == n_local_enhancers:
model_upsample += [
nn.ReflectionPad2d(3),
nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0),
nn.Tanh()
]
setattr(self, 'model' + str(n) + '_1',
nn.Sequential(*model_downsample))
setattr(self, 'model' + str(n) + '_2',
nn.Sequential(*model_upsample))
self.downsample = nn.AvgPool2d(3,
stride=2,
padding=[1, 1],
count_include_pad=False)
def forward(self, input):
# create input pyramid
input_downsampled = [input]
for i in range(self.n_local_enhancers):
input_downsampled.append(self.downsample(input_downsampled[-1]))
# output at coarest level
output_prev = self.model(input_downsampled[-1])
# build up one layer at a time
for n_local_enhancers in range(1, self.n_local_enhancers + 1):
model_downsample = getattr(self,
'model' + str(n_local_enhancers) + '_1')
model_upsample = getattr(self,
'model' + str(n_local_enhancers) + '_2')
input_i = input_downsampled[self.n_local_enhancers -
n_local_enhancers]
output_prev = model_upsample(
model_downsample(input_i) + output_prev)
return output_prev
class GlobalGenerator(pl.LightningModule):
def __init__(self,
input_nc,
output_nc,
ngf=64,
n_downsampling=3,
n_blocks=9,
norm_layer=nn.BatchNorm2d,
padding_type='reflect',
last_op=nn.Tanh()):
assert (n_blocks >= 0)
super(GlobalGenerator, self).__init__()
activation = nn.ReLU(True)
model = [
nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0),
norm_layer(ngf), activation
]
# downsample
for i in range(n_downsampling):
mult = 2**i
model += [
nn.Conv2d(ngf * mult,
ngf * mult * 2,
kernel_size=3,
stride=2,
padding=1),
norm_layer(ngf * mult * 2), activation
]
# resnet blocks
mult = 2**n_downsampling
for i in range(n_blocks):
model += [
ResnetBlock(ngf * mult,
padding_type=padding_type,
activation=activation,
norm_layer=norm_layer)
]
# upsample
for i in range(n_downsampling):
mult = 2**(n_downsampling - i)
model += [
nn.ConvTranspose2d(ngf * mult,
int(ngf * mult / 2),
kernel_size=3,
stride=2,
padding=1,
output_padding=1),
norm_layer(int(ngf * mult / 2)), activation
]
model += [
nn.ReflectionPad2d(3),
nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)
]
if last_op is not None:
model += [last_op]
self.model = nn.Sequential(*model)
def forward(self, input):
return self.model(input)
# Define a resnet block
class ResnetBlock(pl.LightningModule):
def __init__(self,
dim,
padding_type,
norm_layer,
activation=nn.ReLU(True),
use_dropout=False):
super(ResnetBlock, self).__init__()
self.conv_block = self.build_conv_block(dim, padding_type, norm_layer,
activation, use_dropout)
def build_conv_block(self, dim, padding_type, norm_layer, activation,
use_dropout):
conv_block = []
p = 0
if padding_type == 'reflect':
conv_block += [nn.ReflectionPad2d(1)]
elif padding_type == 'replicate':
conv_block += [nn.ReplicationPad2d(1)]
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' %
padding_type)
conv_block += [
nn.Conv2d(dim, dim, kernel_size=3, padding=p),
norm_layer(dim), activation
]
if use_dropout:
conv_block += [nn.Dropout(0.5)]
p = 0
if padding_type == 'reflect':
conv_block += [nn.ReflectionPad2d(1)]
elif padding_type == 'replicate':
conv_block += [nn.ReplicationPad2d(1)]
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' %
padding_type)
conv_block += [
nn.Conv2d(dim, dim, kernel_size=3, padding=p),
norm_layer(dim)
]
return nn.Sequential(*conv_block)
def forward(self, x):
out = x + self.conv_block(x)
return out
class Encoder(pl.LightningModule):
def __init__(self,
input_nc,
output_nc,
ngf=32,
n_downsampling=4,
norm_layer=nn.BatchNorm2d):
super(Encoder, self).__init__()
self.output_nc = output_nc
model = [
nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0),
norm_layer(ngf),
nn.ReLU(True)
]
# downsample
for i in range(n_downsampling):
mult = 2**i
model += [
nn.Conv2d(ngf * mult,
ngf * mult * 2,
kernel_size=3,
stride=2,
padding=1),
norm_layer(ngf * mult * 2),
nn.ReLU(True)
]
# upsample
for i in range(n_downsampling):
mult = 2**(n_downsampling - i)
model += [
nn.ConvTranspose2d(ngf * mult,
int(ngf * mult / 2),
kernel_size=3,
stride=2,
padding=1,
output_padding=1),
norm_layer(int(ngf * mult / 2)),
nn.ReLU(True)
]
model += [
nn.ReflectionPad2d(3),
nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0),
nn.Tanh()
]
self.model = nn.Sequential(*model)
def forward(self, input, inst):
outputs = self.model(input)
# instance-wise average pooling
outputs_mean = outputs.clone()
inst_list = np.unique(inst.cpu().numpy().astype(int))
for i in inst_list:
for b in range(input.size()[0]):
indices = (inst[b:b + 1] == int(i)).nonzero() # n x 4
for j in range(self.output_nc):
output_ins = outputs[indices[:, 0] + b, indices[:, 1] + j,
indices[:, 2], indices[:, 3]]
mean_feat = torch.mean(output_ins).expand_as(output_ins)
outputs_mean[indices[:, 0] + b, indices[:, 1] + j,
indices[:, 2], indices[:, 3]] = mean_feat
return outputs_mean
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