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models.py
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import jittor as jt
from jittor import nn
from jittor import init
import networks
def start_grad(model):
for param in model.parameters():
param.start_grad()
def stop_grad(model):
for param in model.parameters():
param.stop_grad()
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
jt.init.gauss_(m.weight, 0.0, 0.02)
elif classname.find("BatchNorm") != -1:
jt.init.gauss_(m.weight, 1.0, 0.02)
jt.init.constant_(m.bias, 0.0)
class UNetDown(nn.Module):
def __init__(self, in_size, out_size, normalize=True, dropout=0.0):
super(UNetDown, self).__init__()
layers = [nn.Conv(in_size, out_size, 4, stride=2, padding=1, bias=False)]
if normalize:
layers.append(nn.InstanceNorm2d(out_size, affine=None))
layers.append(nn.LeakyReLU(scale=0.2))
if dropout:
layers.append(nn.Dropout(dropout))
self.model = nn.Sequential(*layers)
def execute(self, x):
return self.model(x)
class UNetUp(nn.Module):
def __init__(self, in_size, out_size, dropout=0.0):
super(UNetUp, self).__init__()
layers = [nn.ConvTranspose(in_size, out_size, 4, stride=2, padding=1, bias=False),
nn.InstanceNorm2d(out_size, affine=None), nn.ReLU()]
if dropout:
layers.append(nn.Dropout(dropout))
self.model = nn.Sequential(*layers)
def execute(self, x, skip_input):
x = self.model(x)
x = jt.contrib.concat((x, skip_input), dim=1)
return x
class GeneratorUNet(nn.Module):
def __init__(self, in_channels=3, out_channels=3):
super(GeneratorUNet, self).__init__()
self.down1 = UNetDown(in_channels, 64, normalize=False)
self.down2 = UNetDown(64, 128)
self.down3 = UNetDown(128, 256)
self.down4 = UNetDown(256, 512, dropout=0.5)
self.down5 = UNetDown(512, 512, dropout=0.5)
self.down6 = UNetDown(512, 512, dropout=0.5)
self.down7 = UNetDown(512, 512, dropout=0.5)
self.down8 = UNetDown(512, 512, normalize=False, dropout=0.5)
# self.up1 = UNetUp(512, 512, dropout=0.5)
# self.up2 = UNetUp(512, 1024, dropout=0.5)
# self.up3 = UNetUp(1024, 1024, dropout=0.5)
# self.up4 = UNetUp(1024, 1024)
# self.up5 = UNetUp(1024, 1024)
# self.up6 = UNetUp(1024, 512)
# self.up7 = UNetUp(512, 256)
self.up1 = UNetUp(512, 512, dropout=0.5)
self.up2 = UNetUp(1024, 512, dropout=0.5)
self.up3 = UNetUp(1024, 512, dropout=0.5)
self.up4 = UNetUp(1024, 512, dropout=0.5)
self.up5 = UNetUp(1024, 256)
self.up6 = UNetUp(512, 128)
self.up7 = UNetUp(256, 64)
self.final = nn.Sequential(nn.Upsample(scale_factor=2), nn.ZeroPad2d((1, 0, 1, 0)), nn.Conv(128, out_channels, 4, padding=1), nn.Tanh())
for m in self.modules():
weights_init_normal(m)
def execute(self, x):
d1 = self.down1(x)
d2 = self.down2(d1)
d3 = self.down3(d2)
d4 = self.down4(d3)
d5 = self.down5(d4)
d6 = self.down6(d5)
d7 = self.down7(d6)
d8 = self.down8(d7)
u1 = self.up1(d8, d7)
u2 = self.up2(u1, d6)
u3 = self.up3(u2, d5)
u4 = self.up4(u3, d4)
u5 = self.up5(u4, d3)
u6 = self.up6(u5, d2)
u7 = self.up7(u6, d1)
return self.final(u7)
class UnetGenerator(nn.Module):
def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.InstanceNorm2d, use_dropout=False):
super(UnetGenerator, self).__init__()
# construct unet structure
unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer,
innermost=True) # add the innermost layer
for i in range(num_downs - 5): # add intermediate layers with ngf * 8 filters
unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block,
norm_layer=norm_layer, use_dropout=use_dropout)
# gradually reduce the number of filters from ngf * 8 to ngf
unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block,
norm_layer=norm_layer)
unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block,
norm_layer=norm_layer)
unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True,
norm_layer=norm_layer) # add the outermost layer
def execute(self, input):
return self.model(input)
class UnetSkipConnectionBlock(nn.Module):
def __init__(self, outer_nc, inner_nc, input_nc=None,
submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
if input_nc is None:
input_nc = outer_nc
downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4,
stride=2, padding=1, bias=False)
downrelu = nn.LeakyReLU(0.2)
downnorm = norm_layer(inner_nc)
uprelu = nn.ReLU()
upnorm = norm_layer(outer_nc)
if outermost:
upconv = nn.ConvTranspose(inner_nc * 2, outer_nc,
kernel_size=4, stride=2,
padding=1)
down = [downconv]
up = [uprelu, upconv, nn.Tanh()]
model = down + [submodule] + up
elif innermost:
upconv = nn.ConvTranspose(inner_nc, outer_nc,
kernel_size=4, stride=2,
padding=1, bias=False)
down = [downrelu, downconv]
up = [uprelu, upconv, upnorm]
model = down + up
else:
upconv = nn.ConvTranspose(inner_nc * 2, outer_nc,
kernel_size=4, stride=2,
padding=1, bias=False)
down = [downrelu, downconv, downnorm]
up = [uprelu, upconv, upnorm]
if use_dropout:
model = down + [submodule] + up + [nn.Dropout(0.5)]
else:
model = down + [submodule] + up
self.model = nn.Sequential(*model)
def execute(self, x):
if self.outermost:
return self.model(x)
else: # add skip connections
return jt.contrib.concat([x, self.model(x)], 1)
class Discriminator(nn.Module):
def __init__(self, in_channels=3):
super(Discriminator, self).__init__()
def discriminator_block(in_filters, out_filters, stride=2, normalization=True):
'Returns downsampling layers of each discriminator block'
layers = [nn.Conv(in_filters, out_filters, 4, stride=stride, padding=1)]
if normalization:
layers.append(nn.BatchNorm2d(out_filters, eps=1e-05, momentum=0.1, affine=True))
layers.append(nn.LeakyReLU(scale=0.2))
return layers
self.model = nn.Sequential(*discriminator_block((in_channels * 2), 64, normalization=False),
*discriminator_block(64, 128), *discriminator_block(128, 256),
*discriminator_block(256, 512, stride=1), nn.Conv(512, 1, 4, padding=1, bias=False))
for m in self.modules():
weights_init_normal(m)
def execute(self, input):
return self.model(input)