def _make_transition_layer(
self, num_channels_pre_layer, num_channels_cur_layer):
num_branches_cur = len(num_channels_cur_layer)
num_branches_pre = len(num_channels_pre_layer)
transition_layers = []
for i in range(num_branches_cur):
if i < num_branches_pre:
if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
transition_layers.append(nn.Sequential(
nn.Conv2d(num_channels_pre_layer[i],
num_channels_cur_layer[i],
3,
1,
1,
bias=False),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(
num_channels_cur_layer[i]),
nn.ReLU(inplace=True)))
else:
transition_layers.append(None)
else:
conv3x3s = []
for j in range(i+1-num_branches_pre):
inchannels = num_channels_pre_layer[-1]
outchannels = num_channels_cur_layer[i] \
if j == i-num_branches_pre else inchannels
conv3x3s.append(nn.Sequential(
nn.Conv2d(
inchannels, outchannels, 3, 2, 1, bias=False),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(outchannels),
nn.ReLU(inplace=True)))
transition_layers.append(nn.Sequential(*conv3x3s))
return nn.ModuleList(transition_layers)
def __init__(self, input_num, num1, num2, dilation_rate, drop_out,
norm_type):
super(_DenseAsppBlock, self).__init__()
self.add_module(
'conv1',
nn.Conv2d(in_channels=input_num, out_channels=num1,
kernel_size=1)),
self.add_module(
'norm1',
ModuleHelper.BatchNorm2d(norm_type=norm_type)(num_features=num1)),
self.add_module('relu1', nn.ReLU(inplace=False)),
self.add_module(
'conv2',
nn.Conv2d(in_channels=num1,
out_channels=num2,
kernel_size=3,
dilation=dilation_rate,
padding=dilation_rate)),
self.add_module(
'norm2',
ModuleHelper.BatchNorm2d(norm_type=norm_type)(
num_features=input_num)),
self.add_module('relu2', nn.ReLU(inplace=False)),
self.drop_rate = drop_out
def __init__(self, num_input_features, growth_rate, bn_size, drop_rate,
norm_type):
super(_DenseLayer, self).__init__()
self.add_module(
'norm1',
ModuleHelper.BatchNorm2d(norm_type=norm_type)(num_input_features)),
self.add_module('relu1', nn.ReLU(inplace=True)),
self.add_module(
'conv1',
nn.Conv2d(num_input_features,
bn_size * growth_rate,
kernel_size=1,
stride=1,
bias=False)),
self.add_module(
'norm2',
ModuleHelper.BatchNorm2d(norm_type=norm_type)(bn_size *
growth_rate)),
self.add_module('relu2', nn.ReLU(inplace=True)),
self.add_module(
'conv2',
nn.Conv2d(bn_size * growth_rate,
growth_rate,
kernel_size=3,
stride=1,
padding=1,
bias=False)),
self.drop_rate = drop_rate
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes)
self.downsample = downsample
self.stride = stride
def __init__(self, **kwargs):
super(HighResolutionNet, self).__init__()
self.num_features = 720
# stem net
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1,
bias=False)
self.bn1 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(64)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1,
bias=False)
self.bn2 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(64)
self.relu = nn.ReLU(inplace=True)
self.stage1_cfg = {'NUM_MODULES':1, 'NUM_BRANCHES':1, 'BLOCK':'BOTTLENECK',
'NUM_BLOCKS':[4], 'NUM_CHANNELS':[64], 'FUSE_METHOD':'SUM'}
num_channels = self.stage1_cfg['NUM_CHANNELS'][0]
block = blocks_dict[self.stage1_cfg['BLOCK']]
num_blocks = self.stage1_cfg['NUM_BLOCKS'][0]
self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
stage1_out_channel = block.expansion*num_channels
self.stage2_cfg = {'NUM_MODULES':1, 'NUM_BRANCHES':2, 'BLOCK':'BASIC',
'NUM_BLOCKS':[4,4], 'NUM_CHANNELS':[48,96], 'FUSE_METHOD':'SUM'}
num_channels = self.stage2_cfg['NUM_CHANNELS']
block = blocks_dict[self.stage2_cfg['BLOCK']]
num_channels = [
num_channels[i] * block.expansion for i in range(len(num_channels))]
self.transition1 = self._make_transition_layer(
[stage1_out_channel], num_channels)
self.stage2, pre_stage_channels = self._make_stage(
self.stage2_cfg, num_channels)
self.stage3_cfg = {'NUM_MODULES':4, 'NUM_BRANCHES':3, 'BLOCK':'BASIC',
'NUM_BLOCKS':[4,4,4], 'NUM_CHANNELS':[48,96,192], 'FUSE_METHOD':'SUM'}
num_channels = self.stage3_cfg['NUM_CHANNELS']
block = blocks_dict[self.stage3_cfg['BLOCK']]
num_channels = [
num_channels[i] * block.expansion for i in range(len(num_channels))]
self.transition2 = self._make_transition_layer(
pre_stage_channels, num_channels)
self.stage3, pre_stage_channels = self._make_stage(
self.stage3_cfg, num_channels)
self.stage4_cfg = {'NUM_MODULES':3, 'NUM_BRANCHES':4, 'BLOCK':'BASIC',
'NUM_BLOCKS':[4,4,4,4], 'NUM_CHANNELS':[48,96,192,384], 'FUSE_METHOD':'SUM'}
num_channels = self.stage4_cfg['NUM_CHANNELS']
block = blocks_dict[self.stage4_cfg['BLOCK']]
num_channels = [
num_channels[i] * block.expansion for i in range(len(num_channels))]
self.transition3 = self._make_transition_layer(
pre_stage_channels, num_channels)
self.stage4, pre_stage_channels = self._make_stage(
self.stage4_cfg, num_channels, multi_scale_output=True)
last_inp_channels = np.int(np.sum(pre_stage_channels))
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes)
self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1,
bias=False)
self.bn3 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def __init__(self, input_nc, ndf=64, n_layers=3, norm_type=None):
"""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__()
use_bias = (norm_type == 'instancenorm')
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),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(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),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(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 __init__(self, input_nc, ndf=64, norm_type=None):
"""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__()
use_bias = (norm_type == 'instancenorm')
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),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(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 _make_layer(self, block, planes, blocks, stride=1, norm_type=None):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes *
block.expansion),
)
layers = []
layers.append(
block(self.inplanes,
planes,
stride,
downsample,
norm_type=norm_type))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, norm_type=norm_type))
return nn.Sequential(*layers)
def build_conv_block(self, dim, padding_type, norm_type, use_dropout,
use_bias):
"""Construct a convolutional block.
Parameters:
dim (int) -- the number of channels in the conv layer.
padding_type (str) -- the name of padding layer: reflect | replicate | zero
norm_layer -- normalization layer
use_dropout (bool) -- if use dropout layers.
use_bias (bool) -- if the conv layer uses bias or not
Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
"""
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, bias=use_bias),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(dim),
nn.ReLU(True)
]
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, bias=use_bias),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(dim)
]
return nn.Sequential(*conv_block)
def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000, norm_type=None):
super(DenseNet, self).__init__()
# First convolution
self.features = nn.Sequential(OrderedDict([
('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
('norm0', ModuleHelper.BatchNorm2d(norm_type=norm_type)(num_init_features)),
('relu0', nn.ReLU(inplace=True)),
('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
]))
# Each denseblock
num_features = num_init_features
for i, num_layers in enumerate(block_config):
block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,
bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate, norm_type=norm_type)
self.features.add_module('denseblock%d' % (i + 1), block)
num_features = num_features + num_layers * growth_rate
if i != len(block_config) - 1:
trans = _Transition(num_input_features=num_features,
num_output_features=num_features // 2, norm_type=norm_type)
avg_pool = nn.AvgPool2d(kernel_size=2, stride=2)
self.features.add_module('transition%d' % (i + 1), trans)
self.features.add_module('transition%s_pool' % (i + 1), avg_pool)
num_features = num_features // 2
self.num_features = num_features
# Final batch norm
self.features.add_module('norm5', ModuleHelper.BatchNorm2d(norm_type=norm_type)(num_features))
# Linear layer
self.classifier = nn.Linear(num_features, num_classes)
# Official init from torch repo.
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, ModuleHelper.BatchNorm2d(norm_type=norm_type, ret_cls=True)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.constant_(m.bias, 0)
def _make_fuse_layers(self):
if self.num_branches == 1:
return None
num_branches = self.num_branches
num_inchannels = self.num_inchannels
fuse_layers = []
for i in range(num_branches if self.multi_scale_output else 1):
fuse_layer = []
for j in range(num_branches):
if j > i:
fuse_layer.append(nn.Sequential(
nn.Conv2d(num_inchannels[j],
num_inchannels[i],
1,
1,
0,
bias=False),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(num_inchannels[i])))
elif j == i:
fuse_layer.append(None)
else:
conv3x3s = []
for k in range(i-j):
if k == i - j - 1:
num_outchannels_conv3x3 = num_inchannels[i]
conv3x3s.append(nn.Sequential(
nn.Conv2d(num_inchannels[j],
num_outchannels_conv3x3,
3, 2, 1, bias=False),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(num_outchannels_conv3x3)))
else:
num_outchannels_conv3x3 = num_inchannels[j]
conv3x3s.append(nn.Sequential(
nn.Conv2d(num_inchannels[j],
num_outchannels_conv3x3,
3, 2, 1, bias=False),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(num_outchannels_conv3x3),
nn.ReLU(inplace=True)))
fuse_layer.append(nn.Sequential(*conv3x3s))
fuse_layers.append(nn.ModuleList(fuse_layer))
return nn.ModuleList(fuse_layers)
def freeze_bn(net, norm_type=None):
for m in net.modules():
if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d) or isinstance(m, nn.BatchNorm3d):
m.eval()
if norm_type is not None:
from model.tools.module_helper import ModuleHelper
if isinstance(m, ModuleHelper.BatchNorm2d(norm_type=norm_type, ret_cls=True)) \
or isinstance(m, ModuleHelper.BatchNorm1d(norm_type=norm_type, ret_cls=True)) \
or isinstance(m, ModuleHelper.BatchNorm3d(norm_type=norm_type, ret_cls=True)):
m.eval()
def __init__(self, num_input_features, num_output_features, norm_type):
super(_Transition, self).__init__()
self.add_module(
'norm',
ModuleHelper.BatchNorm2d(norm_type=norm_type)(num_input_features))
self.add_module('relu', nn.ReLU(inplace=True))
self.add_module(
'conv',
nn.Conv2d(num_input_features,
num_output_features,
kernel_size=1,
stride=1,
bias=False))
def __init__(self, low_in_channels, high_in_channels, out_channels, key_channels, value_channels, dropout,
sizes=([1]), norm_type=None,psp_size=(1,3,6,8)):
super(AFNB, self).__init__()
self.stages = []
self.norm_type = norm_type
self.psp_size=psp_size
self.stages = nn.ModuleList(
[self._make_stage([low_in_channels, high_in_channels], out_channels, key_channels, value_channels, size) for
size in sizes])
self.conv_bn_dropout = nn.Sequential(
nn.Conv2d(out_channels + high_in_channels, out_channels, kernel_size=1, padding=0),
ModuleHelper.BatchNorm2d(norm_type=self.norm_type)(out_channels),
nn.Dropout2d(dropout)
)
def __init__(self,
in_channel,
out_channel,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
norm_type=None):
super(ConvBn, self).__init__()
self.conv_bn = nn.Sequential(
nn.Conv2d(in_channel, out_channel, kernel_size, stride, padding,
dilation, groups, False),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(out_channel))
def _make_one_branch(self, branch_index, block, num_blocks, num_channels,
stride=1):
downsample = None
if stride != 1 or \
self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.num_inchannels[branch_index],
num_channels[branch_index] * block.expansion,
kernel_size=1, stride=stride, bias=False),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(num_channels[branch_index] * block.expansion),
)
layers = []
layers.append(block(self.num_inchannels[branch_index],
num_channels[branch_index], stride, downsample))
self.num_inchannels[branch_index] = \
num_channels[branch_index] * block.expansion
for i in range(1, num_blocks[branch_index]):
layers.append(block(self.num_inchannels[branch_index],
num_channels[branch_index]))
return nn.Sequential(*layers)
def __init__(self,
block,
layers,
num_classes=1000,
deep_base=False,
norm_type=None):
super(ResNet, self).__init__()
self.inplanes = 128 if deep_base else 64
if deep_base:
self.prefix = nn.Sequential(
OrderedDict([
('conv1',
nn.Conv2d(3,
64,
kernel_size=3,
stride=2,
padding=1,
bias=False)),
('bn1', ModuleHelper.BatchNorm2d(norm_type=norm_type)(64)),
('relu1', nn.ReLU(inplace=False)),
('conv2',
nn.Conv2d(64,
64,
kernel_size=3,
stride=1,
padding=1,
bias=False)),
('bn2', ModuleHelper.BatchNorm2d(norm_type=norm_type)(64)),
('relu2', nn.ReLU(inplace=False)),
('conv3',
nn.Conv2d(64,
128,
kernel_size=3,
stride=1,
padding=1,
bias=False)),
('bn3', ModuleHelper.BatchNorm2d(norm_type=norm_type)(
self.inplanes)), ('relu3', nn.ReLU(inplace=False))
]))
else:
self.prefix = nn.Sequential(
OrderedDict([('conv1',
nn.Conv2d(3,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)),
('bn1',
ModuleHelper.BatchNorm2d(norm_type=norm_type)(
self.inplanes)),
('relu', nn.ReLU(inplace=False))]))
self.maxpool = nn.MaxPool2d(kernel_size=3,
stride=2,
padding=1,
ceil_mode=True) # change.
self.layer1 = self._make_layer(block,
64,
layers[0],
norm_type=norm_type)
self.layer2 = self._make_layer(block,
128,
layers[1],
stride=2,
norm_type=norm_type)
self.layer3 = self._make_layer(block,
256,
layers[2],
stride=2,
norm_type=norm_type)
self.layer4 = self._make_layer(block,
512,
layers[3],
stride=2,
norm_type=norm_type)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(
m,
ModuleHelper.BatchNorm2d(norm_type=norm_type,
ret_cls=True)):
m.weight.data.fill_(1)
m.bias.data.zero_()
def __init__(self,
input_nc,
output_nc,
ngf=64,
norm_type=None,
use_dropout=False,
n_blocks=6,
padding_type='reflect'):
"""Construct a Resnet-based generator
Parameters:
input_nc (int) -- the number of channels in input images
output_nc (int) -- the number of channels in output images
ngf (int) -- the number of filters in the last conv layer
norm_layer -- normalization layer
use_dropout (bool) -- if use dropout layers
n_blocks (int) -- the number of ResNet blocks
padding_type (str) -- the name of padding layer in conv layers: reflect | replicate | zero
"""
assert (n_blocks >= 0)
super(ResNetGenerator, self).__init__()
use_bias = (norm_type == 'instancenorm')
model = [
nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(ngf),
nn.ReLU(True)
]
n_downsampling = 2
for i in range(n_downsampling): # add downsampling layers
mult = 2**i
model += [
nn.Conv2d(ngf * mult,
ngf * mult * 2,
kernel_size=3,
stride=2,
padding=1,
bias=use_bias),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(ngf * mult * 2),
nn.ReLU(True)
]
mult = 2**n_downsampling
for i in range(n_blocks): # add ResNet blocks
model += [
ResnetBlock(ngf * mult,
padding_type=padding_type,
norm_type=norm_type,
use_dropout=use_dropout,
use_bias=use_bias)
]
for i in range(n_downsampling): # add upsampling layers
mult = 2**(n_downsampling - i)
model += [
nn.ConvTranspose2d(ngf * mult,
int(ngf * mult / 2),
kernel_size=3,
stride=2,
padding=1,
output_padding=1,
bias=use_bias),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(int(ngf * mult /
2)),
nn.ReLU(True)
]
model += [nn.ReflectionPad2d(3)]
model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
model += [nn.Tanh()]
self.model = nn.Sequential(*model)
def __init__(self,
outer_nc,
inner_nc,
input_nc=None,
submodule=None,
outermost=False,
innermost=False,
norm_type=None,
use_dropout=False):
"""Construct a Unet submodule with skip connections.
Parameters:
outer_nc (int) -- the number of filters in the outer conv layer
inner_nc (int) -- the number of filters in the inner conv layer
input_nc (int) -- the number of channels in input images/features
submodule (UnetSkipConnectionBlock) -- previously defined submodules
outermost (bool) -- if this module is the outermost module
innermost (bool) -- if this module is the innermost module
norm_layer -- normalization layer
user_dropout (bool) -- if use dropout layers.
"""
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
use_bias = (norm_type == 'instancenorm')
if input_nc is None:
input_nc = outer_nc
downconv = nn.Conv2d(input_nc,
inner_nc,
kernel_size=4,
stride=2,
padding=1,
bias=use_bias)
downrelu = nn.LeakyReLU(0.2, True)
downnorm = ModuleHelper.BatchNorm2d(norm_type=norm_type)(inner_nc)
uprelu = nn.ReLU(True)
upnorm = ModuleHelper.BatchNorm2d(norm_type=norm_type)(outer_nc)
if outermost:
upconv = nn.ConvTranspose2d(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.ConvTranspose2d(inner_nc,
outer_nc,
kernel_size=4,
stride=2,
padding=1,
bias=use_bias)
down = [downrelu, downconv]
up = [uprelu, upconv, upnorm]
model = down + up
else:
upconv = nn.ConvTranspose2d(inner_nc * 2,
outer_nc,
kernel_size=4,
stride=2,
padding=1,
bias=use_bias)
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)
