def __init__(
self,
in_channels=[1024, 1024],
mid_channels=[1024, 1024],
out_channels=2048,
ds_scales=[(1, 1, 1), (1, 1, 1)],
):
super(LevelFusion, self).__init__()
ops = []
num_ins = len(in_channels)
for i in range(num_ins):
op = Downampling(in_channels[i],
mid_channels[i],
kernel_size=(1, 1, 1),
stride=(1, 1, 1),
padding=(0, 0, 0),
bias=False,
groups=32,
norm=True,
activation=True,
downsample_position='before',
downsample_scale=ds_scales[i])
ops.append(op)
self.ops = Sequential(*ops)
in_dims = np.sum(mid_channels)
self.fusion_conv = Sequential(
nn.Conv3D(in_dims, out_channels, 1, 1, 0, bias_attr=False),
nn.BatchNorm(out_channels), Relu())
def __init__(
self,
inplanes=[1024, 2048],
planes=2048,
):
super(SpatialModulation, self).__init__()
op = []
for i, dim in enumerate(inplanes):
ds_factor = planes // dim
ds_num = int(np.log2(ds_factor))
# print('ds_num',ds_num)
# if ds_num < 1:
# # None
# op.append(Identity())
#
# else:
# for dsi in range(ds_num):
# print('dsi',dsi)
# in_factor = 2 ** dsi
# out_factor = 2 ** (dsi + 1)
# op.append(ConvModule(dim * in_factor, dim * out_factor, kernel_size=(1, 3, 3), stride=(1, 2, 2),
# padding=(0, 1, 1), bias=False))
# sub_layers.append(op)
for j in range(ds_num):
op.append(
ConvModule(1024,
2048,
kernel_size=(1, 3, 3),
stride=(1, 2, 2),
padding=(0, 1, 1),
bias=False))
op = Sequential(*op)
self.spatial_modulation = Sequential(op)
def __init__(self,
in_channels,
hidden_channels,
kernel_size,
num_layers,
batch_first=True,
return_all_layers=False,
training=True):
super(ConvLSTM, self).__init__()
self.cell_list = Sequential()
self._check_kernel_size_consistency(kernel_size)
# Make sure that both `kernel_size` and `hidden_dim` are lists having len == num_layers
kernel_size = self._extend_for_multilayer(kernel_size, num_layers)
hidden_channels = self._extend_for_multilayer(hidden_channels,
num_layers)
if not len(kernel_size) == len(hidden_channels) == num_layers:
raise ValueError('Inconsistent list length.')
self.input_dim = in_channels
self.hidden_dim = hidden_channels
self.kernel_size = kernel_size
self.num_layers = num_layers
self.batch_first = batch_first
self.return_all_layers = return_all_layers
for i in range(0, self.num_layers):
cur_input_dim = self.input_dim if i == 0 else self.hidden_dim[i -
1]
self.cell_list.add_sublayer(name='{}'.format(i),
sublayer=ConvLSTMCell(
in_channels=cur_input_dim,
hidden_channels=self.hidden_dim[i],
kernel_size=self.kernel_size[i],
training=training))
def make_res_layer(block,
inplanes,
planes,
blocks,
spatial_stride=1,
temporal_stride=1,
dilation=1,
style='pytorch',
inflate_freq=1,
inflate_style='3x1x1',
nonlocal_freq=1,
nonlocal_cfg=None,
with_cp=False):
inflate_freq = inflate_freq if not isinstance(inflate_freq, int) else (inflate_freq,) * blocks
nonlocal_freq = nonlocal_freq if not isinstance(nonlocal_freq, int) else (nonlocal_freq,) * blocks
assert len(inflate_freq) == blocks
assert len(nonlocal_freq) == blocks
downsample = None
if spatial_stride != 1 or inplanes != planes * block.expansion:
downsample = Sequential(
nn.Conv3D(
inplanes,
planes * block.expansion,
filter_size=1,
stride=(temporal_stride, spatial_stride, spatial_stride),
bias_attr=False),
nn.BatchNorm(planes * block.expansion),
)
layers = []
layers.append(
block(
inplanes,
planes,
spatial_stride,
temporal_stride,
dilation,
downsample,
style=style,
if_inflate=(inflate_freq[0] == 1),
inflate_style=inflate_style,
if_nonlocal=(nonlocal_freq[0] == 1),
nonlocal_cfg=nonlocal_cfg,
with_cp=with_cp))
inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(
block(inplanes,
planes,
1, 1,
dilation,
style=style,
if_inflate=(inflate_freq[i] == 1),
inflate_style=inflate_style,
if_nonlocal=(nonlocal_freq[i] == 1),
nonlocal_cfg=nonlocal_cfg,
with_cp=with_cp))
return Sequential(*layers)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = Sequential(
Conv2D(self.inplanes, planes * block.expansion, filter_size=1, stride=stride, bias_attr=False),
BatchNorm(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return Sequential(*layers)
def __init__(self, sequence_list):
super(LinConPoo, self).__init__()
self.__sequence_list = copy.deepcopy(sequence_list)
if not isinstance(self.__sequence_list, list):
raise ValueError('sequence_list error')
self._layers_squence = Sequential()
self._layers_list = []
LAYLIST = [ConvBNLayer, Conv2D, Linear, Pool2D]
for i, layer_arg in enumerate(self.__sequence_list):
if isinstance(layer_arg, dict):
layer_class = layer_arg.pop('type')
if not layer_class in LAYLIST:
raise KeyError(
"the parameters of sequence_list must be within `[ConvBNLayer, Conv2D, Linear, Pool2D]`"
)
layer_obj = layer_class(**layer_arg)
elif isinstance(layer_arg, list):
layer_class = layer_arg.pop(0)
if not layer_class in LAYLIST:
raise KeyError(
"the parameters of sequence_list must be within `[ConvBNLayer, Conv2D, Linear, Pool2D]`"
)
layer_obj = layer_class(*layer_arg)
else:
raise ValueError("sequence_list error")
layer_name = layer_class.__name__ + str(i)
self._layers_list.append((layer_name, layer_obj))
self._layers_squence.add_sublayer(*(layer_name, layer_obj))
self._layers_squence = Sequential(*self._layers_list)
def architecture_init(self):
# shared layers
ch_in = self.channels
shared_sub_layers = [
Conv2D(num_channels=3,
num_filters=ch_in,
filter_size=3,
padding=1,
param_attr=weight_initializer,
bias_attr=bias_initializer,
stride=1,
act=None)
]
for i in range(self.repeat_num):
ch_out = min(ch_in * 2, self.max_conv_dim)
sub_layer = ResBlock(ch_in,
ch_out,
normalize=False,
downsample=True,
sn=self.sn,
act=None)
ch_in = ch_out
shared_sub_layers.append(sub_layer)
shared_sub_layers.append(Leaky_Relu(alpha=0.2))
shared_sub_layers.append(
Conv2D(num_channels=self.max_conv_dim,
num_filters=self.max_conv_dim,
filter_size=4,
padding=0,
param_attr=weight_initializer,
bias_attr=bias_initializer,
stride=1,
act=None))
shared_sub_layers.append(Leaky_Relu(alpha=0.2))
shared_layers = Sequential(*shared_sub_layers)
# unshared layers
unshared_sub_layers = []
for _ in range(self.num_domains):
sub_layer = Linear(self.max_conv_dim,
self.style_dim,
param_attr=weight_initializer,
bias_attr=bias_initializer,
act=None)
unshared_sub_layers.append(sub_layer)
unshared_layers = Sequential(*unshared_sub_layers)
return shared_layers, unshared_layers
def __init__(self, dim, use_bias):
super(ResnetBlock, self).__init__()
conv_block = []
conv_block += [
ReflectionPad2d([1, 1, 1, 1]),
Conv2D(dim,
dim,
filter_size=3,
stride=1,
padding=0,
bias_attr=use_bias),
InstanceNorm(dim),
ReLU(True)
]
#TODO: 这里加一个不带ReLU的有何意义
conv_block += [
ReflectionPad2d([1, 1, 1, 1]),
Conv2D(dim,
dim,
filter_size=3,
stride=1,
padding=0,
bias_attr=use_bias),
InstanceNorm(dim)
]
self.conv_block = Sequential(*conv_block)
def __init__(self, dim, use_bias):
super(ResnetBlock, self).__init__()
conv_block = []
conv_block += [
ReflectionPad2d(pad=1),
Conv2D(num_channels=dim,
num_filters=dim,
filter_size=3,
stride=1,
padding=0,
bias_attr=use_bias),
InstanceNorm(dim),
ReLU(inplace=True)
]
conv_block += [
ReflectionPad2d(pad=1),
Conv2D(num_channels=dim,
num_filters=dim,
filter_size=3,
stride=1,
padding=0,
bias_attr=use_bias),
InstanceNorm(dim)
]
self.conv_block = Sequential(*conv_block)
def __init__(self, input_nc, ndf=64, n_layers=5):
super(Discriminator, self).__init__()
model = [ReflectionPad2d([1,1,1,1]),
# TODO 谱归一化
Conv2D(input_nc, ndf, filter_size=4, stride=2, padding=0, bias_attr=True),
LeakyReLU(0.2, True)]
for i in range(1, n_layers - 2):
mult = 2 ** (i - 1)
model += [ReflectionPad2d([1,1,1,1]),
Conv2D(ndf * mult, ndf * mult * 2, filter_size=4, stride=2, padding=0, bias_attr=True),
LeakyReLU(0.2, True)]
mult = 2 ** (n_layers - 2 - 1)
model += [ReflectionPad2d([1,1,1,1]),
Conv2D(ndf * mult, ndf * mult * 2, filter_size=4, stride=1, padding=0, bias_attr=True),
LeakyReLU(0.2, True)]
# Class Activation Map
mult = 2 ** (n_layers - 2)
self.gap_fc = Linear(ndf * mult, 1, bias_attr=False)
self.gmp_fc = Linear(ndf * mult, 1, bias_attr=False)
self.conv1x1 = Conv2D(ndf * mult * 2, ndf * mult, filter_size=1, stride=1, bias_attr=True)
self.leaky_relu = LeakyReLU(0.2, True)
self.pad = ReflectionPad2d([1,1,1,1])
self.conv = Conv2D(ndf * mult, 1, filter_size=4, stride=1, padding=0, bias_attr=False)
self.model = Sequential(*model)
def __init__(self, dim, use_bias):
super(ResnetBlock, self).__init__()
conv_block = []
conv_block += [
ReflectionPad2D(1),
Conv2D(dim,
num_filters=dim,
filter_size=3,
stride=1,
bias_attr=use_bias),
Instancenorm(),
ReLU()
]
conv_block += [
ReflectionPad2D(1),
Conv2D(dim,
num_filters=dim,
filter_size=3,
stride=1,
bias_attr=use_bias),
Instancenorm()
]
self.conv_block = Sequential(*conv_block)
def __init__(self, in_nc=64, out_nc=64, light=True, use_bias=True):
super(MLP, self).__init__()
# ops for Gamma, Beta block
self.light = light
FC = [
Linear(in_nc,
out_nc,
param_attr=init_w(),
bias_attr=init_bias(use_bias),
act='relu'),
Linear(out_nc,
out_nc,
param_attr=init_w(),
bias_attr=init_bias(use_bias),
act='relu')
]
self.gamma = Linear(out_nc,
out_nc,
param_attr=init_w(),
bias_attr=init_bias(use_bias)) # FC256
self.beta = Linear(out_nc,
out_nc,
param_attr=init_w(),
bias_attr=init_bias(use_bias)) # FC256
self.FC = Sequential(*FC)
def __init__(self, dim, use_bias=True):
super(ResnetBlock, self).__init__()
conv_block = []
conv_block += [
ReflectionPad2d(1),
Conv2D(dim,
dim,
filter_size=3,
stride=1,
padding=0,
param_attr=init_w(),
bias_attr=init_bias(use_bias)),
InstanceNorm(dim),
ReLU()
]
conv_block += [
ReflectionPad2d(1),
Conv2D(dim,
dim,
filter_size=3,
stride=1,
padding=0,
param_attr=init_w(),
bias_attr=init_bias(use_bias)),
InstanceNorm(dim)
]
self.conv_block = Sequential(*conv_block)
def __init__(self, dim, use_bias):
super(ResnetBlock, self).__init__()
conv_block = []
conv_block += [
ReflectionPad2d(1),
Conv2D(dim,
dim,
filter_size=3,
stride=1,
padding=0,
bias_attr=use_bias),
InstanceNorm(dim),
PRelu(mode="all")
]
conv_block += [
ReflectionPad2d(1),
Conv2D(dim,
dim,
filter_size=3,
stride=1,
padding=0,
bias_attr=use_bias),
InstanceNorm(dim)
]
self.conv_block = Sequential(*conv_block)
def __init__(self, dim, use_bias):
super(ResnetBlock, self).__init__()
conv_block = []
conv_block += [
#fluid.layers.pad2d(1),
ReflectionPad2d(1),
Conv2D(dim,
dim,
filter_size=3,
stride=1,
padding=0,
bias_attr=use_bias),
InstanceNorm(dim),
ReLU(False)
#BatchNorm(dim,act='relu')
]
conv_block += [
#fluid.layers.pad2d(1),
ReflectionPad2d(1),
Conv2D(dim,
dim,
filter_size=3,
stride=1,
padding=0,
bias_attr=use_bias),
InstanceNorm(dim)
]
self.conv_block = Sequential(*conv_block)
def architecture_init(self):
layers = []
layers.append(BatchNorm(self.in_planes))
layers.append(Relu())
layers.append(Conv2D(self.in_planes, self.out_planes, 1, 1, bias_attr=bias_initializer_1x1))
downsample = Sequential(*layers)
return downsample
def architecture_init(self):
ch_in = self.channels
ch_out = self.channels
encoder = []
decoder = []
# down/up-sampling blocks
for i in range(self.repeat_num):
ch_out = min(ch_in * 2, self.max_conv_dim)
encoder.append(
ResBlock(ch_in,
ch_out,
normalize=True,
downsample=True,
sn=self.sn))
decoder.insert(0,
AdainResBlock(ch_out,
ch_in,
upsample=True,
sn=self.sn,
w_hpf=self.w_hpf)) # stack-like
ch_in = ch_out
# bottleneck blocks
for i in range(2):
encoder.append(ResBlock(ch_out, ch_out, normalize=True,
sn=self.sn))
decoder.insert(
0, AdainResBlock(ch_out, ch_out, sn=self.sn, w_hpf=self.w_hpf))
to_rgb_layer = []
to_rgb_layer.append(InstanceNorm(self.style_dim))
to_rgb_layer.append(Leaky_Relu(alpha=0.2))
to_rgb_layer.append(
Conv2D(num_channels=self.style_dim,
num_filters=self.img_ch,
filter_size=1,
padding=0,
param_attr=weight_initializer,
bias_attr=bias_initializer,
stride=1,
act=None))
encoders = Sequential(*encoder)
decoders = Sequential(*decoder)
to_rgb = Sequential(*to_rgb_layer)
return encoders, decoders, to_rgb
def __init__(self, input_nc, ndf=64, n_layers=5):
super(Discriminator, self).__init__()
model = [
ReflectionPad2D(1),
Spectralnorm(layer=Conv2D(input_nc,
num_filters=ndf,
filter_size=4,
stride=2,
bias_attr=True)),
LeakyReLU(alpha=0.2)
]
for i in range(1, n_layers - 2):
mult = 2**(i - 1)
model += [
ReflectionPad2D(1),
Spectralnorm(layer=Conv2D(ndf * mult,
num_filters=ndf * mult * 2,
filter_size=4,
stride=2,
bias_attr=True)),
LeakyReLU(alpha=0.2)
]
mult = 2**(n_layers - 2 - 1)
model += [
ReflectionPad2D(1),
Spectralnorm(layer=Conv2D(ndf * mult,
num_filters=ndf * mult * 2,
filter_size=4,
stride=1,
bias_attr=True)),
LeakyReLU(alpha=0.2)
]
# Class Activation Map
mult = 2**(n_layers - 2)
self.gap_fc = Spectralnorm(layer=Linear(ndf *
mult, 1, bias_attr=False))
self.gmp_fc = Spectralnorm(layer=Linear(ndf *
mult, 1, bias_attr=False))
self.conv1x1 = Conv2D(ndf * mult * 2,
num_filters=ndf * mult,
filter_size=1,
stride=1,
bias_attr=True)
self.leaky_relu = LeakyReLU(alpha=0.2)
self.pad = ReflectionPad2D(1)
self.conv = Spectralnorm(layer=Conv2D(ndf * mult,
num_filters=1,
filter_size=4,
stride=1,
bias_attr=False))
self.model = Sequential(*model)
def __make_layer(self, in_dim, cfg):
in_planes = in_dim
layer_list = []
for layer in cfg:
for out_planes in layer:
layer_list.append(BasicConv(in_planes, out_planes))
in_planes = out_planes
layer_list.append(
Pool2D(pool_size=2, pool_type='max', pool_stride=2))
return Sequential(*layer_list)
def __init__(self, channels, stride):
'''
channels: in_channels == out_channels for MixedOp
'''
super().__init__()
self._ops = LayerList()
for prim_op in PRIMITIVES:
op = OPS[prim_op](channels, stride, False)
if 'pool' in prim_op:
gama, beta = bn_param_config()
bn = BatchNorm(channels, param_attr=gama, bias_attr=beta)
op = Sequential(op, bn)
self._ops.append(op)
def _make_layer(self, block, planes, blocks, shortcut_type, stride=1):
downsample = None
if stride != 1 or self.in_planes != planes * block.expansion:
if shortcut_type == 'A':
# 这里下采样我是用1x1卷积做，这里先不写
pass
else:
downsample = Sequential(
conv1x1x1(self.in_planes, planes * block.expansion, stride),
BatchNorm(planes * block.expansion))
layers = []
layers.append(
block(in_planes=self.in_planes,
planes=planes,
stride=stride,
downsample=downsample))
self.in_planes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.in_planes, planes))
return Sequential(*layers)
def architecture_init(self):
shared_sub_layers = [
Linear(self.latent_dim,
self.hidden_dim,
param_attr=weight_initializer,
bias_attr=bias_initializer,
act=None)
]
shared_sub_layers.append(Relu())
for i in range(3):
shared_sub_layers.append(
Linear(self.hidden_dim,
self.hidden_dim,
param_attr=weight_initializer,
bias_attr=bias_initializer,
act=None))
shared_sub_layers.append(Relu())
shared_layers = Sequential(*shared_sub_layers)
unshared_layer = []
for n_d in range(self.num_domains):
unshared_sub_layers = []
for i in range(3):
unshared_sub_layers.append(
Linear(self.hidden_dim,
self.hidden_dim,
param_attr=weight_initializer,
bias_attr=bias_initializer,
act=None))
unshared_sub_layers.append(Relu())
unshared_sub_layers.append(
Linear(self.hidden_dim,
self.style_dim,
param_attr=weight_initializer,
bias_attr=bias_initializer,
act=None))
unshared_layer.append(Sequential(*unshared_sub_layers))
unshared_layers = Sequential(*unshared_layer)
return shared_layers, unshared_layers
def __init__(self,
gene,
in_channels,
init_node_c,
out_channels,
depth,
n_nodes,
drop_rate=0):
'''
gene: Genotype, searched architecture of a cell
in_channels: RGB channel.
init_node_c: Initial number of filters or output channels for the node.
out_channels: How many classes are there in the target.
depth: Number of cells.
n_nodes: Number of nodes in each cell.
drop_rate: dropout rate.
'''
super().__init__()
stem_c = min(in_channels, n_nodes) * init_node_c # stem out_channels
self.stem = Sequential(
Conv2D(in_channels,
stem_c,
3,
padding=1,
param_attr=ParamAttr(initializer=MSRAInitializer()),
bias_attr=False), BatchNorm(stem_c))
c0 = c1 = stem_c
node_c = init_node_c # node out_channels
self.cells = LayerList()
reduction_prev = False
reduce_layers = [depth // 3, 2 * depth // 3]
for i in range(depth):
if i in reduce_layers:
node_c *= 2
reduction = True
else:
reduction = False
cell = SearchedCell(gene, n_nodes, c0, c1, node_c, reduction,
reduction_prev, drop_rate)
reduction_prev = reduction
self.cells.append(cell)
c0, c1 = c1, cell.out_channels
self.global_pooling = Pool2D(pool_type='avg', global_pooling=True)
self.classifier = Linear(
input_dim=c1,
output_dim=out_channels,
param_attr=ParamAttr(initializer=MSRAInitializer()),
bias_attr=ParamAttr(initializer=MSRAInitializer()))
def __init__(self, name_scope, out_chs=20, in_chs=1024, inter_chs=512):
super(DANet, self).__init__(name_scope)
name_scope = self.full_name()
self.in_chs = in_chs
self.out_chs = out_chs
self.inter_chs = inter_chs if inter_chs else in_chs
self.backbone = ResNet(50)
self.conv5p = Sequential(
Conv2D(self.in_chs, self.inter_chs, 3, padding=1),
BatchNorm(self.inter_chs, act='relu'),
)
self.conv5c = Sequential(
Conv2D(self.in_chs, self.inter_chs, 3, padding=1),
BatchNorm(self.inter_chs, act='relu'),
)
self.sp = PAM_module(self.inter_chs)
self.sc = CAM_module(self.inter_chs)
self.conv6p = Sequential(
Conv2D(self.inter_chs, self.inter_chs, 3, padding=1),
BatchNorm(self.inter_chs, act='relu'),
)
self.conv6c = Sequential(
Conv2D(self.inter_chs, self.inter_chs, 3, padding=1),
BatchNorm(self.inter_chs, act='relu'),
)
self.conv7p = Sequential(
Dropout(0.1),
Conv2D(self.inter_chs, self.out_chs, 1),
)
self.conv7c = Sequential(
Dropout(0.1),
Conv2D(self.inter_chs, self.out_chs, 1),
)
self.conv7pc = Sequential(
Dropout(0.1),
Conv2D(self.inter_chs, self.out_chs, 1),
)
def __init__(self,
input_nc,
output_nc,
ngf=64,
n_blocks=6,
img_size=256,
light=False):
assert (n_blocks >= 0)
super(ResnetGenerator, self).__init__()
self.input_nc = input_nc
self.output_nc = output_nc
self.ngf = ngf
self.n_blocks = n_blocks
self.img_size = img_size
self.light = light
DownBlock = []
DownBlock += [
#fluid.layers.pad2d(3),
ReflectionPad2d(3),
Conv2D(num_channels=input_nc,
num_filters=ngf,
filter_size=7,
stride=1,
padding=0,
bias_attr=False),
InstanceNorm(ngf),
ReLU(False)
#BatchNorm(ngf,act='relu')
#fluid.layers.instance_norm(ngf)
]
# self.conv1=Conv2D(input_nc, ngf, 7)
# self.instance_norm=InstanceNorm(ngf)
#self.n_downsampling=n_downsampling
# Down-Sampling
n_downsampling = 2
for i in range(n_downsampling):
mult = 2**i
DownBlock += [
#fluid.layers.pad2d(1),
ReflectionPad2d(1),
Conv2D(ngf * mult,
ngf * mult * 2,
filter_size=3,
stride=2,
padding=0,
bias_attr=False),
InstanceNorm(ngf * mult * 2),
ReLU(False)
#BatchNorm(ngf * mult * 2,act='relu')
#fluid.layers.instance_norm(ngf * mult * 2)
]
# Down-Sampling Bottleneck
mult = 2**n_downsampling
for i in range(n_blocks):
DownBlock += [ResnetBlock(ngf * mult, use_bias=False)]
#self.renetblock=ResnetBlock(ngf * mult, use_bias=False)
# Class Activation Map
self.gap_fc = Linear(ngf * mult, 1, bias_attr=False)
self.gmp_fc = Linear(ngf * mult, 1, bias_attr=False)
self.conv1x1 = Conv2D(ngf * mult * 2,
ngf * mult,
filter_size=1,
stride=1,
bias_attr=True)
self.relu = ReLU(False)
# Gamma, Beta block
if self.light:
FC = [
Linear(ngf * mult, ngf * mult, bias_attr=False, act='relu'),
Linear(ngf * mult, ngf * mult, bias_attr=False, act='relu')
]
else:
FC = [
Linear(img_size // mult * img_size // mult * ngf * mult,
ngf * mult,
bias_attr=False,
act='relu'),
Linear(ngf * mult, ngf * mult, bias_attr=False, act='relu')
]
self.gamma = Linear(ngf * mult, ngf * mult, bias_attr=False)
self.beta = Linear(ngf * mult, ngf * mult, bias_attr=False)
# Up-Sampling Bottleneck
for i in range(n_blocks):
setattr(self, 'UpBlock1_' + str(i + 1),
ResnetAdaILNBlock(ngf * mult, use_bias=False))
# Up-Sampling
UpBlock2 = []
for i in range(n_downsampling):
mult = 2**(n_downsampling - i)
UpBlock2 += [ #nn.Upsample(scale_factor=2, mode='nearest'),
#fluid.layers.pad2d(1),
Upsample(),
ReflectionPad2d(1),
Conv2D(ngf * mult,
int(ngf * mult / 2),
filter_size=3,
stride=1,
padding=0,
bias_attr=False),
ILN(int(ngf * mult / 2)),
ReLU(False)
]
UpBlock2 += [
#fluid.layers.pad2d(3),
ReflectionPad2d(3),
Conv2D(ngf,
output_nc,
filter_size=7,
stride=1,
padding=0,
bias_attr=False),
Tanh()
]
self.DownBlock = Sequential(*DownBlock)
self.FC = Sequential(*FC)
self.UpBlock2 = Sequential(*UpBlock2)
def __init__(self, input_nc, output_nc, ngf=64, n_blocks=6, img_size=256, light=False):
super(ResnetGenerator, self).__init__()
'''
Args:
input_cn: 输入通道数
output_nc: 输出通道数，此处二者都为3
ngf: base channel number per layer
n_blocks: The number of resblock
'''
assert(n_blocks >= 0)
super(ResnetGenerator, self).__init__()
self.input_nc = input_nc
self.output_nc = output_nc
self.ngf = ngf
self.n_blocks = n_blocks
self.img_size = img_size
self.light = light
DownBlock = []
# 3 ReflectionPad2d 抵消了紧接着的7*7Conv层
#TODO 此处由于Paddle的pad2d在fluid.layer中，不能作为对象定义，比较麻烦，因此暂时使用普通padding
DownBlock += [ReflectionPad2d([1,1,1,1]),
Conv2D(input_nc, ngf, filter_size=7, stride=1, padding=0, bias_attr=False),
InstanceNorm(ngf),
#TODO paddle没有单独的ReLU对象，暂时用PReLU代替，后期将const改成0
ReLU(True)]
# Down-Sampling
n_downsampling = 2
for i in range(n_downsampling):
mult = 2**i
# 通道以2倍增，stride为2，大小以2减少
DownBlock += [ReflectionPad2d([1,1,1,1]),
Conv2D(ngf * mult, ngf * mult * 2, filter_size=3, stride=2, padding=0, bias_attr=False),
InstanceNorm(ngf * mult * 2),
ReLU(True)]
# Down-Sampling Bottleneck
mult = 2**n_downsampling
for i in range(n_blocks):
DownBlock += [ResnetBlock(ngf * mult, use_bias=False)]
# Class Activation Map
self.gap_fc = Linear(ngf * mult, 1, bias_attr=False)
self.gmp_fc = Linear(ngf * mult, 1, bias_attr=False)
self.conv1x1 = Conv2D(ngf * mult * 2, ngf * mult, filter_size=1, stride=1, padding=0, bias_attr=True)
self.relu = ReLU(True)
# Gamma, Beta block
if self.light:
FC = [Linear(ngf * mult, ngf * mult, bias_attr=False),
ReLU(True),
Linear(ngf * mult, ngf * mult, bias_attr=False),
ReLU(True)]
else:
# TODO 这里没太理解
FC = [Linear(img_size // mult * img_size // mult * ngf * mult, ngf * mult, bias_attr=False),
ReLU(True),
Linear(ngf * mult, ngf * mult, bias_attr=False),
ReLU(True)]
self.gamma = Linear(ngf * mult, ngf * mult, bias_attr=False)
self.beta = Linear(ngf * mult, ngf * mult, bias_attr=False)
# Up-Sampling Bottleneck
for i in range(n_blocks):
setattr(self, 'UpBlock1_' + str(i+1), ResnetAdaILNBlock(ngf * mult, use_bias=False))
# Up-Sampling
UpBlock2 = []
for i in range(n_downsampling):
mult = 2**(n_downsampling - i)
UpBlock2 += [Upsample(scale_factor=2),
ReflectionPad2d([1,1,1,1]),
Conv2D(ngf * mult, int(ngf * mult / 2), filter_size=3, stride=1, padding=0, bias_attr=False),
ILN(int(ngf * mult / 2)),
ReLU(True)]
UpBlock2 += [ReflectionPad2d([3,3,3,3]),
Conv2D(ngf, output_nc, filter_size=7, stride=1, padding=0, bias_attr=False),
Tanh(False)]
self.DownBlock = Sequential(*DownBlock)
self.FC = Sequential(*FC)
self.UpBlock2 = Sequential(*UpBlock2)
def __init__(self,
in_channels=[256, 512, 1024, 2048],
out_channels=256,
spatial_modulation_config=None,
temporal_modulation_config=None,
upsampling_config=None,
downsampling_config=None,
level_fusion_config=None,
aux_head_config=None,
mode=None):
super(TPN, self).__init__()
assert isinstance(in_channels, list)
assert isinstance(out_channels, int)
self.in_channels = in_channels
self.out_channels = out_channels
self.num_ins = len(in_channels)
self.mode = mode
# spatial_modulation_config = Config(spatial_modulation_config) if isinstance(spatial_modulation_config,
# dict) else spatial_modulation_config
# temporal_modulation_config = Config(temporal_modulation_config) if isinstance(temporal_modulation_config,
# dict) else temporal_modulation_config
# upsampling_config = Config(upsampling_config) if isinstance(upsampling_config, dict) else upsampling_config
# downsampling_config = Config(downsampling_config) if isinstance(downsampling_config,
# dict) else downsampling_config
# aux_head_config = Config(aux_head_config) if isinstance(aux_head_config, dict) else aux_head_config
# level_fusion_config = Config(level_fusion_config) if isinstance(level_fusion_config,
# dict) else level_fusion_config
# self.temporal_modulation_ops = nn.ModuleList()
# self.upsampling_ops = nn.ModuleList()
# self.downsampling_ops = nn.ModuleList()
temp_modulation_ops = []
temp_upsampling_ops = []
temp_downsampling_ops = []
for i in range(0, self.num_ins, 1):
inplanes = in_channels[-1]
planes = out_channels
if temporal_modulation_config is not None:
# overwrite the temporal_modulation_config
# print(temporal_modulation_config)
temporal_modulation_config['param'][
'downsample_scale'] = temporal_modulation_config['scales'][
i]
temporal_modulation_config['param']['inplanes'] = inplanes
temporal_modulation_config['param']['planes'] = planes
temporal_modulation = TemporalModulation(
**temporal_modulation_config['param'])
temp_modulation_ops.append(temporal_modulation)
self.temporal_modulation_ops = Sequential(*temp_modulation_ops)
if i < self.num_ins - 1:
if upsampling_config is not None:
# overwrite the upsampling_config
upsampling = Upsampling(**upsampling_config)
temp_upsampling_ops.append(upsampling)
self.upsampling_ops = Sequential(*temp_upsampling_ops)
if downsampling_config is not None:
# overwrite the downsampling_config
downsampling_config['param']['inplanes'] = planes
downsampling_config['param']['planes'] = planes
downsampling_config['param'][
'downsample_scale'] = downsampling_config['scales']
downsampling = Downampling(**downsampling_config['param'])
temp_downsampling_ops.append(downsampling)
self.downsampling_ops = Sequential(*temp_downsampling_ops)
self.level_fusion_op = LevelFusion() # **level_fusion_config
self.spatial_modulation = SpatialModulation(
) # **spatial_modulation_config
out_dims = level_fusion_config['out_channels']
# Two pyramids
self.level_fusion_op2 = LevelFusion(**level_fusion_config)
self.pyramid_fusion_op = Sequential(
nn.Conv3D(out_dims * 2, 2048, 1, 1, 0, bias_attr=False),
nn.BatchNorm(2048), Relu())
# overwrite aux_head_config
if aux_head_config is not None:
aux_head_config['inplanes'] = self.in_channels[-2]
self.aux_head = AuxHead(**aux_head_config)
else:
self.aux_head = None
def __init__(self, input_nc, output_nc, ngf=64, n_blocks=6, img_size=256, light=False):
assert(n_blocks >= 0)
super(ResnetGenerator, self).__init__()
self.input_nc = input_nc
self.output_nc = output_nc
self.ngf = ngf
self.n_blocks = n_blocks
self.img_size = img_size
self.light = light
DownBlock = []
DownBlock += [
ReflectionPad2D(3),
Conv2D(num_channels=input_nc, num_filters=ngf, filter_size=7, stride=1, padding=0, bias_attr=False),
InstanceNorm(self.ngf),
Relu(),
]
# Down-Sampling
n_downsampling = 2
for i in range(n_downsampling):
mult = 2**i
DownBlock += [
ReflectionPad2D(1),
Conv2D(num_channels=ngf * mult, num_filters=ngf * mult * 2, filter_size=3, stride=2, padding=0, bias_attr=False),
InstanceNorm(ngf * mult * 2),
Relu(),
]
# Down-Sampling Bottleneck
mult = 2**n_downsampling
for i in range(n_blocks):
DownBlock += [ResnetBlock(ngf * mult, use_bias=False)]
# Class Activation Map
self.gap_fc = Linear(ngf * mult, 1, bias_attr=False, act='sigmoid')
self.gmp_fc = Linear(ngf * mult, 1, bias_attr=False, act='sigmoid')
self.conv1x1 = Conv2D(ngf * mult * 2, ngf * mult, filter_size=1, stride=1, bias_attr=True)
self.relu = Relu()
# Gamma, Beta block
if self.light:
FC = [
Linear(ngf * mult, ngf * mult, bias_attr=False),
Relu(),
Linear(ngf * mult, ngf * mult, bias_attr=False),
Relu(),
]
else:
FC = [
Linear(img_size // mult * img_size // mult * ngf * mult, ngf * mult, bias_attr=False),
Relu(),
Linear(ngf * mult, ngf * mult, bias_attr=False),
Relu(),
]
self.gamma = Linear(ngf * mult, ngf * mult, bias_attr=False)
self.beta = Linear(ngf * mult, ngf * mult, bias_attr=False)
# Up-Sampling Bottleneck
for i in range(n_blocks):
setattr(self, 'UpBlock1_' + str(i+1), ResnetAdaILNBlock(ngf * mult, use_bias=False))
# Up-Sampling
UpBlock2 = []
for i in range(n_downsampling):
mult = 2**(n_downsampling - i)
UpBlock2 += [
UpSample(2),
ReflectionPad2D(1),
Conv2D(num_channels=ngf * mult, num_filters=int(ngf * mult / 2),
filter_size=3, stride=1, padding=0, bias_attr=False),
ILN(int(ngf * mult / 2)),
Relu(),
]
UpBlock2 += [
ReflectionPad2D(3),
Conv2D(num_channels=ngf, num_filters=output_nc,
filter_size=7, stride=1, padding=0, bias_attr=False),
Tanh(),
]
self.DownBlock = Sequential(*DownBlock)
self.DownBlock_list = DownBlock
self.FC = Sequential(*FC)
self.UpBlock2 = Sequential(*UpBlock2)
class ConvLSTM(fluid.dygraph.Layer):
def __init__(self,
in_channels,
hidden_channels,
kernel_size,
num_layers,
batch_first=True,
return_all_layers=False,
training=True):
super(ConvLSTM, self).__init__()
self.cell_list = Sequential()
self._check_kernel_size_consistency(kernel_size)
# Make sure that both `kernel_size` and `hidden_dim` are lists having len == num_layers
kernel_size = self._extend_for_multilayer(kernel_size, num_layers)
hidden_channels = self._extend_for_multilayer(hidden_channels,
num_layers)
if not len(kernel_size) == len(hidden_channels) == num_layers:
raise ValueError('Inconsistent list length.')
self.input_dim = in_channels
self.hidden_dim = hidden_channels
self.kernel_size = kernel_size
self.num_layers = num_layers
self.batch_first = batch_first
self.return_all_layers = return_all_layers
for i in range(0, self.num_layers):
cur_input_dim = self.input_dim if i == 0 else self.hidden_dim[i -
1]
self.cell_list.add_sublayer(name='{}'.format(i),
sublayer=ConvLSTMCell(
in_channels=cur_input_dim,
hidden_channels=self.hidden_dim[i],
kernel_size=self.kernel_size[i],
training=training))
def forward(self, input_tensor, hidden_state=None):
"""
Parameters
----------
input_tensor: todo
5-D Tensor (b, t, c, h, w)
hidden_state: todo
None. todo implement stateful
Returns
-------
last_state_list, layer_output
"""
# Implement stateful ConvLSTM
if hidden_state is not None:
raise NotImplementedError()
else:
b, _, _, h, w = input_tensor.shape
hidden_state = self._init_hidden(b, h, w)
layer_output_list = []
last_state_list = []
seq_len = input_tensor.shape[1]
cur_layer_input = input_tensor
for layer_idx in range(self.num_layers):
h, c = hidden_state[layer_idx]
output_inner = []
for t in range(seq_len):
h, c = self.cell_list['{}'.format(layer_idx)](
input_tensor=cur_layer_input[:, t, :, :, :],
cur_state=[h, c])
output_inner.append(h)
layer_output = fluid.layers.stack(output_inner, axis=1)
cur_layer_input = layer_output
layer_output_list.append(layer_output)
last_state_list.append([h, c])
if not self.return_all_layers:
layer_output_list = layer_output_list[-1:]
last_state_list = last_state_list[-1:]
return layer_output_list, last_state_list
def _init_hidden(self, b, h, w):
init_states = []
for i in range(self.num_layers):
init_states.append(self.cell_list[i].init_hidden(b, h, w))
return init_states
@staticmethod
def _check_kernel_size_consistency(kernel_size):
if not (isinstance(kernel_size, tuple) or
(isinstance(kernel_size, list)
and all([isinstance(elem, tuple) for elem in kernel_size]))):
raise ValueError('`kernel_size` must be tuple or list of tuples')
@staticmethod
def _extend_for_multilayer(param, num_layers):
if not isinstance(param, list):
param = [param] * num_layers
return param
def __init__(self, input_nc, ndf=64, n_layers=5):
super(Discriminator, self).__init__()
#model = [fluid.layers.pad2d(1),
#nn.utils.spectral_norm(
#nn.Conv2d(input_nc, ndf, kernel_size=4, stride=2, padding=0, bias=True)),
#nn.LeakyReLU(0.2, True)]
model = [
#fluid.layers.pad2d(1),
ReflectionPad2d(1),
Spectralnorm(
Conv2D(input_nc,
ndf,
filter_size=4,
stride=2,
padding=0,
bias_attr=True)),
LeakyReLU(0.2)
]
#for i in range(1, n_layers - 2):
#mult = 2 ** (i - 1)
#model += [fluid.layers.pad2d(1),
#nn.utils.spectral_norm(
#nn.Conv2d(ndf * mult, ndf * mult * 2, kernel_size=4, stride=2, padding=0, bias=True)),
#nn.LeakyReLU(0.2, True)]
for i in range(1, n_layers - 2):
mult = 2**(i - 1)
model += [
#fluid.layers.pad2d(1),
ReflectionPad2d(1),
Spectralnorm(
Conv2D(ndf * mult,
ndf * mult * 2,
filter_size=4,
stride=2,
padding=0,
bias_attr=True)),
LeakyReLU(0.2)
]
mult = 2**(n_layers - 2 - 1)
#model += [fluid.layers.pad2d(1),
#nn.utils.spectral_norm(
#nn.Conv2d(ndf * mult, ndf * mult * 2, kernel_size=4, stride=1, padding=0, bias=True)),
#nn.LeakyReLU(0.2, True)]
model += [
#fluid.layers.pad2d(1),
ReflectionPad2d(1),
Spectralnorm(
Conv2D(ndf * mult,
ndf * mult * 2,
filter_size=4,
stride=1,
padding=0,
bias_attr=True)),
LeakyReLU(0.2)
]
# Class Activation Map
mult = 2**(n_layers - 2)
#self.gap_fc = nn.utils.spectral_norm(nn.Linear(ndf * mult, 1, bias=False))
#self.gmp_fc = nn.utils.spectral_norm(nn.Linear(ndf * mult, 1, bias=False))
#self.conv1x1 = nn.Conv2d(ndf * mult * 2, ndf * mult, kernel_size=1, stride=1, bias=True)
#self.leaky_relu = nn.LeakyReLU(0.2, True)
self.gap_fc = Spectralnorm(Linear(ndf * mult, 1, bias_attr=False))
self.gmp_fc = Spectralnorm(Linear(ndf * mult, 1, bias_attr=False))
self.conv1x1 = Conv2D(ndf * mult * 2,
ndf * mult,
filter_size=1,
stride=1,
bias_attr=True)
self.leaky_relu = LeakyReLU(0.2)
#self.pad = fluid.layers.pad2d(1)
self.pad = ReflectionPad2d(1)
#self.conv = nn.utils.spectral_norm(
#nn.Conv2d(ndf * mult, 1, kernel_size=4, stride=1, padding=0, bias=False))
self.conv = Spectralnorm(
Conv2D(ndf * mult,
1,
filter_size=4,
stride=1,
padding=0,
bias_attr=False))
self.model = Sequential(*model)
