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AiDA_Python/app/service/image2sketch/models/networks.py

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feat sketch 提取接口 fix
2024-08-14 16:45:34 +08:00
import functools
from torch.nn import init
from torch.optim import lr_scheduler
from .layer import *
###############################################################################
# Helper Functions
###############################################################################
class Identity(nn.Module):
def forward(self, x):
return x
def get_norm_layer(norm_type='instance'):
"""Return a normalization layer
Parameters:
norm_type (str) -- the name of the normalization layer: batch | instance | none
For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).
For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
"""
if norm_type == 'batch':
norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
elif norm_type == 'instance':
norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
elif norm_type == 'none':
def norm_layer(x):
return Identity()
else:
raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
return norm_layer
def get_scheduler(optimizer, opt):
"""Return a learning rate scheduler
Parameters:
optimizer -- the optimizer of the network
opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions．　
opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine
For 'linear', we keep the same learning rate for the first <opt.n_epochs> epochs
and linearly decay the rate to zero over the next <opt.n_epochs_decay> epochs.
For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers.
See https://pytorch.org/docs/stable/optim.html for more details.
"""
if opt.lr_policy == 'linear':
def lambda_rule(epoch):
lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1)
return lr_l
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
elif opt.lr_policy == 'step':
scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1)
elif opt.lr_policy == 'plateau':
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
elif opt.lr_policy == 'cosine':
scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0)
else:
return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)
return scheduler
def init_weights(net, init_type='normal', init_gain=0.02):
"""Initialize network weights.
Parameters:
net (network) -- network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
work better for some applications. Feel free to try yourself.
"""
def init_func(m): # define the initialization function
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if init_type == 'normal':
init.normal_(m.weight.data, 0.0, init_gain)
elif init_type == 'xavier':
init.xavier_normal_(m.weight.data, gain=init_gain)
elif init_type == 'kaiming':
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
init.orthogonal_(m.weight.data, gain=init_gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
init.constant_(m.bias.data, 0.0)
elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
init.normal_(m.weight.data, 1.0, init_gain)
init.constant_(m.bias.data, 0.0)
print('initialize network with %s' % init_type)
net.apply(init_func) # apply the initialization function <init_func>
def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):
"""Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
Parameters:
net (network) -- the network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
gain (float) -- scaling factor for normal, xavier and orthogonal.
gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
Return an initialized network.
"""
if len(gpu_ids) > 0:
assert (torch.cuda.is_available())
net.to(gpu_ids[0])
net = torch.nn.DataParallel(net, gpu_ids) # multi-GPUs
init_weights(net, init_type, init_gain=init_gain)
return net
def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]):
net = None
norm_layer = get_norm_layer(norm_type=norm)
if netG == 'ref_unpair_cbam_cat':
net = ref_unpair(input_nc, output_nc, ngf, norm='inorm', status='ref_unpair_cbam_cat')
elif netG == 'ref_unpair_recon':
net = ref_unpair(input_nc, output_nc, ngf, norm='inorm', status='ref_unpair_recon')
elif netG == 'triplet':
net = triplet(input_nc, output_nc, ngf, norm='inorm')
else:
raise NotImplementedError('Generator model name [%s] is not recognized' % netG)
return init_net(net, init_type, init_gain, gpu_ids)
class AdaIN(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
eps = 1e-5
mean_x = torch.mean(x, dim=[2, 3])
mean_y = torch.mean(y, dim=[2, 3])
std_x = torch.std(x, dim=[2, 3])
std_y = torch.std(y, dim=[2, 3])
mean_x = mean_x.unsqueeze(-1).unsqueeze(-1)
mean_y = mean_y.unsqueeze(-1).unsqueeze(-1)
std_x = std_x.unsqueeze(-1).unsqueeze(-1) + eps
std_y = std_y.unsqueeze(-1).unsqueeze(-1) + eps
out = (x - mean_x) / std_x * std_y + mean_y
return out
class HED(nn.Module):
def __init__(self):
super(HED, self).__init__()
self.moduleVggOne = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False)
)
self.moduleVggTwo = nn.Sequential(
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False)
)
self.moduleVggThr = nn.Sequential(
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False)
)
self.moduleVggFou = nn.Sequential(
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False)
)
self.moduleVggFiv = nn.Sequential(
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False)
)
self.moduleScoreOne = nn.Conv2d(in_channels=64, out_channels=1, kernel_size=1, stride=1, padding=0)
self.moduleScoreTwo = nn.Conv2d(in_channels=128, out_channels=1, kernel_size=1, stride=1, padding=0)
self.moduleScoreThr = nn.Conv2d(in_channels=256, out_channels=1, kernel_size=1, stride=1, padding=0)
self.moduleScoreFou = nn.Conv2d(in_channels=512, out_channels=1, kernel_size=1, stride=1, padding=0)
self.moduleScoreFiv = nn.Conv2d(in_channels=512, out_channels=1, kernel_size=1, stride=1, padding=0)
self.moduleCombine = nn.Sequential(
nn.Conv2d(in_channels=5, out_channels=1, kernel_size=1, stride=1, padding=0),
nn.Sigmoid()
)
def forward(self, tensorInput):
tensorBlue = (tensorInput[:, 2:3, :, :] * 255.0) - 104.00698793
tensorGreen = (tensorInput[:, 1:2, :, :] * 255.0) - 116.66876762
tensorRed = (tensorInput[:, 0:1, :, :] * 255.0) - 122.67891434
tensorInput = torch.cat([tensorBlue, tensorGreen, tensorRed], 1)
tensorVggOne = self.moduleVggOne(tensorInput)
tensorVggTwo = self.moduleVggTwo(tensorVggOne)
tensorVggThr = self.moduleVggThr(tensorVggTwo)
tensorVggFou = self.moduleVggFou(tensorVggThr)
tensorVggFiv = self.moduleVggFiv(tensorVggFou)
tensorScoreOne = self.moduleScoreOne(tensorVggOne)
tensorScoreTwo = self.moduleScoreTwo(tensorVggTwo)
tensorScoreThr = self.moduleScoreThr(tensorVggThr)
tensorScoreFou = self.moduleScoreFou(tensorVggFou)
tensorScoreFiv = self.moduleScoreFiv(tensorVggFiv)
tensorScoreOne = nn.functional.interpolate(input=tensorScoreOne, size=(tensorInput.size(2), tensorInput.size(3)), mode='bilinear', align_corners=False)
tensorScoreTwo = nn.functional.interpolate(input=tensorScoreTwo, size=(tensorInput.size(2), tensorInput.size(3)), mode='bilinear', align_corners=False)
tensorScoreThr = nn.functional.interpolate(input=tensorScoreThr, size=(tensorInput.size(2), tensorInput.size(3)), mode='bilinear', align_corners=False)
tensorScoreFou = nn.functional.interpolate(input=tensorScoreFou, size=(tensorInput.size(2), tensorInput.size(3)), mode='bilinear', align_corners=False)
tensorScoreFiv = nn.functional.interpolate(input=tensorScoreFiv, size=(tensorInput.size(2), tensorInput.size(3)), mode='bilinear', align_corners=False)
return self.moduleCombine(torch.cat([tensorScoreOne, tensorScoreTwo, tensorScoreThr, tensorScoreFou, tensorScoreFiv], 1))
# return self.moduleCombine(torch.cat([ tensorScoreOne, tensorScoreTwo, tensorScoreThr, tensorScoreOne, tensorScoreTwo ], 1))
# return torch.sigmoid(tensorScoreOne),torch.sigmoid(tensorScoreTwo),torch.sigmoid(tensorScoreThr),torch.sigmoid(tensorScoreFou),torch.sigmoid(tensorScoreFiv),self.moduleCombine(torch.cat([ tensorScoreOne, tensorScoreTwo, tensorScoreThr, tensorScoreFou, tensorScoreFiv ], 1))
# return torch.sigmoid(tensorScoreTwo)
def define_HED(init_weights_, gpu_ids_=[]):
net = HED()
if len(gpu_ids_) > 0:
assert (torch.cuda.is_available())
net.to(gpu_ids_[0])
net = torch.nn.DataParallel(net, gpu_ids_) # multi-GPUs
if not init_weights_ == None:
device = torch.device('cuda:{}'.format(gpu_ids_[0])) if gpu_ids_ else torch.device('cpu')
print('Loading model from: %s' % init_weights_)
state_dict = torch.load(init_weights_, map_location=str(device))
if isinstance(net, torch.nn.DataParallel):
net.module.load_state_dict(state_dict)
else:
net.load_state_dict(state_dict)
print('load the weights successfully')
return net
def define_styletps(init_weights_, gpu_ids_=[], shape=False):
net = None
if shape == False:
net = triplet()
if len(gpu_ids_) > 0:
assert (torch.cuda.is_available())
net.to(gpu_ids_[0])
net = torch.nn.DataParallel(net, gpu_ids_) # multi-GPUs
if not init_weights_ == None:
device = torch.device('cuda:{}'.format(gpu_ids_[0])) if gpu_ids_ else torch.device('cpu')
print('Loading model from: %s' % init_weights_)
state_dict = torch.load(init_weights_, map_location=str(device))
if isinstance(net, torch.nn.DataParallel):
net.module.load_state_dict(state_dict)
else:
net.load_state_dict(state_dict)
print('load the weights successfully')
return net
class triplet(nn.Module):
def __init__(self): # mnblk=4
super(triplet, self).__init__()
# self.channels = nch_in
self.nch_in = 1
self.nch_out = 1
self.nch_ker = 64
self.norm = 'bnorm'
# self.nblk = nblk
if self.norm == 'bnorm':
self.bias = False
else:
self.bias = True
self.conv0 = CNR2d(self.nch_in, self.nch_ker, kernel_size=7, stride=1, padding=3, norm=self.norm, relu=0.0)
self.conv1 = CNR2d(self.nch_ker, 2 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)
self.conv2 = CNR2d(2 * self.nch_ker, 4 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)
self.final_pool = nn.AdaptiveAvgPool2d((1, 1))
self.linear = nn.Linear(256, 128)
def forward(self, x, y, z):
x = self.conv0(x)
x = self.conv1(x)
x = self.conv2(x)
x = self.final_pool(x)
x = torch.flatten(x, 1)
x = self.linear(x)
y = self.conv0(y)
y = self.conv1(y)
y = self.conv2(y)
y = self.final_pool(y)
y = torch.flatten(y, 1)
y = self.linear(y)
z = self.conv0(z)
z = self.conv1(z)
z = self.conv2(z)
z = self.final_pool(z)
z = torch.flatten(z, 1)
z = self.linear(z)
return x, y, z
class MLP(nn.Module):
def __init__(self, input_dim, output_dim, dim, n_blk, norm='none', activ='relu'):
super(MLP, self).__init__()
self.model = []
self.model += [LinearBlock(input_dim, dim, norm=norm, activation=activ)]
for i in range(n_blk - 2):
self.model += [LinearBlock(dim, dim, norm=norm, activation=activ)]
self.model += [LinearBlock(dim, output_dim, norm='none', activation='none')] # no output activations
self.model = nn.Sequential(*self.model)
def forward(self, x):
return self.model(x.view(x.size(0), -1))
class ref_unpair(nn.Module):
def __init__(self, nch_in, nch_out, nch_ker=64, norm='bnorm', nblk=4, status='ref_unpair'):
super(ref_unpair, self).__init__()
nch_ker = 64
# self.channels = nch_in
self.nch_in = nch_in
self.nchs_in = 1
self.status = status
if self.status == 'ref_unpair_recon':
self.nch_out = 3
self.nch_in = 1
else:
self.nch_out = 1
self.nch_ker = nch_ker
self.norm = norm
self.nblk = nblk
self.dec0 = []
if status == 'ref_unpair_cbam_cat':
self.cbam_c = CBAM(nch_ker * 8, 16, 3, cbam_status="channel")
self.cbam_s = CBAM(nch_ker * 8, 16, 3, cbam_status="spatial")
self.enc1_s = CNR2d(self.nchs_in, self.nch_ker, kernel_size=7, stride=1, padding=3, norm=self.norm, relu=0.0)
self.enc2_s = CNR2d(self.nch_ker, 2 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)
self.enc3_s = CNR2d(2 * self.nch_ker, 4 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)
self.enc4_s = CNR2d(4 * self.nch_ker, 8 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)
if norm == 'bnorm':
self.bias = False
else:
self.bias = True
self.enc1_c = CNR2d(self.nch_in, self.nch_ker, kernel_size=7, stride=1, padding=3, norm=self.norm, relu=0.0)
self.enc2_c = CNR2d(self.nch_ker, 2 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)
self.enc3_c = CNR2d(2 * self.nch_ker, 4 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)
self.enc4_c = CNR2d(4 * self.nch_ker, 8 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)
if status == 'ref_unpair_cbam_cat':
self.res_cat1 = ResBlock_cat(8 * self.nch_ker, 8 * self.nch_ker, kernel_size=3, stride=1, padding=1, norm=self.norm, relu=0.0, padding_mode='reflection')
self.res_cat2 = ResBlock_cat(8 * self.nch_ker, 8 * self.nch_ker, kernel_size=3, stride=1, padding=1, norm=self.norm, relu=0.0, padding_mode='reflection')
self.res_cat3 = ResBlock_cat(8 * self.nch_ker, 8 * self.nch_ker, kernel_size=3, stride=1, padding=1, norm=self.norm, relu=0.0, padding_mode='reflection')
self.res_cat4 = ResBlock_cat(8 * self.nch_ker, 8 * self.nch_ker, kernel_size=3, stride=1, padding=1, norm=self.norm, relu=0.0, padding_mode='reflection')
if self.nblk and status != 'ref_unpair_cbam_cat':
res = []
for i in range(self.nblk):
res += [ResBlock(8 * self.nch_ker, 8 * self.nch_ker, kernel_size=3, stride=1, padding=1, norm=self.norm, relu=0.0, padding_mode='reflection')]
self.res1 = nn.Sequential(*res)
# self.dec0 += [DECNR2d(16 * self.nch_ker, 8 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)]
self.dec0 += [DECNR2d(8 * self.nch_ker, 4 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)]
self.dec0 += [DECNR2d(4 * self.nch_ker, 2 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)]
self.dec0 += [DECNR2d(2 * self.nch_ker, 1 * self.nch_ker, kernel_size=4, stride=2, padding=1, norm=self.norm, relu=0.0)]
self.dec0 += [DECNR2d(1 * self.nch_ker, 1 * self.nch_ker, kernel_size=7, stride=1, padding=3, norm=self.norm, relu=0.0)]
self.dec0 += [nn.Conv2d(1 * self.nch_ker, self.nch_out, kernel_size=3, stride=1, padding=1)]
self.dec = nn.Sequential(*self.dec0)
def forward(self, content, style):
content_cs = self.enc1_c(content)
content_cs = self.enc2_c(content_cs)
content_cs = self.enc3_c(content_cs)
content_cs = self.enc4_c(content_cs)
# content_cs = self.enc5_c(content_cs)
if self.status == 'ref_unpair_cbam_cat':
cbam_content_cs = self.cbam_s(content_cs)
sp_content_cs = content_cs + cbam_content_cs
style_cs = self.enc1_s(style)
style_cs = self.enc2_s(style_cs)
style_cs = self.enc3_s(style_cs)
style_cs = self.enc4_s(style_cs)
cbam_style_cs = self.cbam_c(style_cs)
ch_style_cs = style_cs + cbam_style_cs
content_output = self.adaptive_instance_normalization(content_cs, style_cs)
cbam_content_output = self.adaptive_instance_normalization(sp_content_cs, ch_style_cs)
content_output = self.res_cat1(content_output, cbam_content_output)
content_output = self.res_cat2(content_output, cbam_content_output)
content_output = self.res_cat3(content_output, cbam_content_output)
content_output = self.res_cat4(content_output, cbam_content_output)
else:
content_output = content_cs
if self.nblk and self.status != 'ref_unpair_cbam_cat':
content_cs = self.res1(content_output)
content_output = self.dec(content_output)
content_output = torch.tanh(content_output)
return content_output
def calc_mean_std(self, feat, eps=1e-5):
# eps is a small value added to the variance to avoid divide-by-zero.
size = feat.size()
assert (len(size) == 4)
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
return feat_mean, feat_std
def adaptive_instance_normalization(self, content_feat, style_feat):
assert (content_feat.size()[:2] == style_feat.size()[:2])
size = content_feat.size()
style_mean, style_std = self.calc_mean_std(style_feat)
content_mean, content_std = self.calc_mean_std(content_feat)
normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
return normalized_feat * style_std.expand(size) + style_mean.expand(size)
def define_D(input_nc, ndf, netD, n_layers_D=3, norm='batch', init_type='normal', init_gain=0.02, gpu_ids=[]):
net = None
norm_layer = get_norm_layer(norm_type=norm)
if netD == 'basic': # default PatchGAN classifier
net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer)
elif netD == 'n_layers': # more options
net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer)
elif netD == 'pixel': # classify if each pixel is real or fake
net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer)
else:
raise NotImplementedError('Discriminator model name [%s] is not recognized' % netD)
return init_net(net, init_type, init_gain, gpu_ids)
##############################################################################
# Classes
##############################################################################
class GANLoss(nn.Module):
"""Define different GAN objectives.
The GANLoss class abstracts away the need to create the target label tensor
that has the same size as the input.
"""
def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
""" Initialize the GANLoss class.
Parameters:
gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp.
target_real_label (bool) - - label for a real image
target_fake_label (bool) - - label of a fake image
Note: Do not use sigmoid as the last layer of Discriminator.
LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss.
"""
super(GANLoss, self).__init__()
self.register_buffer('real_label', torch.tensor(target_real_label))
self.register_buffer('fake_label', torch.tensor(target_fake_label))
self.gan_mode = gan_mode
if gan_mode == 'lsgan':
self.loss = nn.MSELoss()
elif gan_mode == 'vanilla':
self.loss = nn.BCEWithLogitsLoss()
elif gan_mode in ['wgangp']:
self.loss = None
else:
raise NotImplementedError('gan mode %s not implemented' % gan_mode)
def get_target_tensor(self, prediction, target_is_real):
if target_is_real:
target_tensor = self.real_label
else:
target_tensor = self.fake_label
return target_tensor.expand_as(prediction)
def __call__(self, prediction, target_is_real):
if self.gan_mode in ['lsgan', 'vanilla']:
target_tensor = self.get_target_tensor(prediction, target_is_real)
loss = self.loss(prediction, target_tensor)
elif self.gan_mode == 'wgangp':
if target_is_real:
loss = -prediction.mean()
else:
loss = prediction.mean()
return loss
def cal_gradient_penalty(netD, real_data, fake_data, device, type='mixed', constant=1.0, lambda_gp=10.0):
if lambda_gp > 0.0:
if type == 'real': # either use real images, fake images, or a linear interpolation of two.
interpolatesv = real_data
elif type == 'fake':
interpolatesv = fake_data
elif type == 'mixed':
alpha = torch.rand(real_data.shape[0], 1, device=device)
alpha = alpha.expand(real_data.shape[0], real_data.nelement() // real_data.shape[0]).contiguous().view(*real_data.shape)
interpolatesv = alpha * real_data + ((1 - alpha) * fake_data)
else:
raise NotImplementedError('{} not implemented'.format(type))
interpolatesv.requires_grad_(True)
disc_interpolates = netD(interpolatesv)
gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolatesv,
grad_outputs=torch.ones(disc_interpolates.size()).to(device),
create_graph=True, retain_graph=True, only_inputs=True)
gradients = gradients[0].view(real_data.size(0), -1) # flat the data
gradient_penalty = (((gradients + 1e-16).norm(2, dim=1) - constant) ** 2).mean() * lambda_gp # added eps
return gradient_penalty, gradients
else:
return 0.0, None
class NLayerDiscriminator(nn.Module):
"""Defines a PatchGAN discriminator"""
def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d):
"""Construct a PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
n_layers (int) -- the number of conv layers in the discriminator
norm_layer -- normalization layer
"""
super(NLayerDiscriminator, self).__init__()
if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
kw = 4
padw = 1
sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
nf_mult = 1
nf_mult_prev = 1
for n in range(1, n_layers): # gradually increase the number of filters
nf_mult_prev = nf_mult
nf_mult = min(2 ** n, 8)
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
nf_mult_prev = nf_mult
nf_mult = min(2 ** n_layers, 8)
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map
self.model = nn.Sequential(*sequence)
def forward(self, input):
"""Standard forward."""
return self.model(input)
class PixelDiscriminator(nn.Module):
"""Defines a 1x1 PatchGAN discriminator (pixelGAN)"""
def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d):
"""Construct a 1x1 PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
norm_layer -- normalization layer
"""
super(PixelDiscriminator, self).__init__()
if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
self.net = [
nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0),
nn.LeakyReLU(0.2, True),
nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias),
norm_layer(ndf * 2),
nn.LeakyReLU(0.2, True),
nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)]
self.net = nn.Sequential(*self.net)
def forward(self, input):
"""Standard forward."""
return self.net(input)
class CBAM(nn.Module):
def __init__(self, n_channels_in, reduction_ratio, kernel_size, cbam_status):
super(CBAM, self).__init__()
self.n_channels_in = n_channels_in
self.reduction_ratio = reduction_ratio
self.kernel_size = kernel_size
self.channel_attention = ChannelAttention_nopara(n_channels_in, reduction_ratio)
self.spatial_attention = SpatialAttention_nopara(kernel_size)
self.status = cbam_status
def forward(self, x):
## We don't use cbam in this version
if self.status == "cbam":
chan_att = self.channel_attention(x)
fp = chan_att * x
spat_att = self.spatial_attention(fp)
fpp = spat_att * fp
if self.status == "spatial":
spat_att = self.spatial_attention(x) # * s_para_1d
fpp = spat_att * x
if self.status == "channel":
chan_att = self.channel_attention(x) # * c_para_1d
fpp = chan_att * x
return fpp # ,c_wgt,s_wgt
class SpatialAttention_nopara(nn.Module):
def __init__(self, kernel_size):
super(SpatialAttention_nopara, self).__init__()
self.kernel_size = kernel_size
assert kernel_size % 2 == 1, "Odd kernel size required"
self.conv = nn.Conv2d(in_channels=2, out_channels=1, kernel_size=kernel_size, padding=int((kernel_size - 1) / 2))
def forward(self, x):
max_pool = self.agg_channel(x, "max")
avg_pool = self.agg_channel(x, "avg")
pool = torch.cat([max_pool, avg_pool], dim=1)
conv = self.conv(pool)
conv = conv.repeat(1, x.size()[1], 1, 1)
att = torch.sigmoid(conv)
return att
def agg_channel(self, x, pool="max"):
b, c, h, w = x.size()
x = x.view(b, c, h * w)
x = x.permute(0, 2, 1)
if pool == "max":
x = F.max_pool1d(x, c)
elif pool == "avg":
x = F.avg_pool1d(x, c)
x = x.permute(0, 2, 1)
x = x.view(b, 1, h, w)
return x
class ChannelAttention_nopara(nn.Module):
def __init__(self, n_channels_in, reduction_ratio):
super(ChannelAttention_nopara, self).__init__()
self.n_channels_in = n_channels_in
self.reduction_ratio = reduction_ratio
self.middle_layer_size = int(self.n_channels_in / float(self.reduction_ratio))
self.bottleneck = nn.Sequential(
nn.Linear(self.n_channels_in, self.middle_layer_size),
nn.ReLU(),
nn.Linear(self.middle_layer_size, self.n_channels_in)
)
def forward(self, x):
kernel = (x.size()[2], x.size()[3])
avg_pool = F.avg_pool2d(x, kernel)
max_pool = F.max_pool2d(x, kernel)
avg_pool = avg_pool.view(avg_pool.size()[0], -1)
max_pool = max_pool.view(max_pool.size()[0], -1)
avg_pool_bck = self.bottleneck(avg_pool)
max_pool_bck = self.bottleneck(max_pool)
pool_sum = avg_pool_bck + max_pool_bck
sig_pool = torch.sigmoid(pool_sum)
sig_pool = sig_pool.unsqueeze(2).unsqueeze(3)
# out = sig_pool.repeat(1,1,kernel[0], kernel[1])
return sig_pool
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