def __init__(self,
layer,
buffer,
in_channels,
out_channels,
kernel_size,
stride=1,
dilation=1,
groups=1,
bias=True,
padding=0,
norm='in',
activation='elu',
pad_type='zero'):
super(RConv, self).__init__()
self.buffer = buffer
self.layer = layer
self.conv_1 = conv_block(in_channels, out_channels, kernel_size,
stride, dilation, groups, bias, padding,
"none", "none", pad_type)
self.conv_2 = conv_layer(out_channels,
out_channels,
kernel_size,
stride=1,
dilation=1,
groups=1)
self.fusion = conv_block(out_channels * 2,
out_channels,
kernel_size=1,
norm=norm,
activation=activation)
self.bn_act = nn.Sequential(nn.InstanceNorm2d(out_channels), nn.ELU())
def __init__(self, layer, buffer, in_channels, out_channels, kernel_size=3, stride=2, dilation=1, groups=1,
bias=True, padding=1, output_padding=1, norm='in', activation='elu', pad_type='zero'):
"""
:param layer:
:param buffer:
:param in_channels:
:param out_channels:
:param kernel_size:
:param stride:
:param dilation:
:param groups:
:param bias:
:param padding:
:param output_padding:
:param norm:
:param activation:
:param pad_type:
"""
super(RDeConv, self).__init__()
self.buffer = buffer
self.layer = layer
self.deconv = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride,
padding=padding, output_padding=output_padding, dilation=dilation, groups=groups, bias=bias)
self.act = _activation(activation)
self.norm = _norm(norm, out_channels)
self.conv = conv_block(out_channels, out_channels, kernel_size, stride=1, bias=bias,
padding=1, pad_type=pad_type, norm=norm, activation=activation)
self.fusion = conv_block(out_channels * 2, out_channels, kernel_size=1, norm=norm,
activation=activation)
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=0,
bias=True,
pad_type='zero',
norm='none',
activation='relu'):
super(upconv_block, self).__init__()
self.deconv = nn.ConvTranspose2d(in_channels, out_channels, 4, 2, 1)
self.act = _activation('relu')
self.norm = _norm('in', out_channels)
self.conv = conv_block(out_channels,
out_channels,
kernel_size,
stride,
bias=bias,
padding=padding,
pad_type=pad_type,
norm=norm,
activation=activation)
def __init__(self,
nf,
res_blocks_num=8,
dmfb_blocks_num=4,
attention_mode="self"):
"""
:param nf: number of channels
:param res_blocks_num: number of Residual blocks
:param dmfb_blocks_num: number of DMFB blocks
:param attention_mode: Attention mode ("context" or "self")
"""
super(FineBottleneck, self).__init__()
self.attention = SelfAttention(nf, k=8)
res_seq = []
for _ in range(res_blocks_num):
block = B.ResConv(nf, kernel_size=3, dilation=2,
padding=2) # [192, 64, 64]
res_seq.append(block)
self.res_seq = nn.Sequential(*res_seq)
dmfb_seq = []
for _ in range(dmfb_blocks_num):
block = DMFB(nf) # [192, 64, 64]
dmfb_seq.append(block)
self.dmfb_seq = nn.Sequential(*dmfb_seq)
self.out = nn.Sequential(
conv_block(3 * nf,
nf,
kernel_size=1,
stride=1,
padding=0,
norm="in",
activation="elu",
pad_type="zero"),
conv_block(nf,
nf,
kernel_size=3,
stride=1,
padding=1,
norm="in",
activation="elu",
pad_type="zero"))
def __init__(self,
in_channels_r, out_channels_r, in_channels_u, out_channels,
kernel_size_in, kernel_size_out,
up_stride_in, stride_out,
up_padding_in, padding_out, output_padding=0,
activation_in='lrelu', activation_out='lrelu',
norm_in='bn', norm_out='none'):
"""
u {l-1} and r {l} new should be of equal sizes to be concatenated
:param in_channels_r: in channels for r {l}
:param out_channels_r: out channels for r {l-1}
:param in_channels_u: in channels for u {l-1}
:param out_channels: out channels for (u {l-1}, r {l}) -> r {l-1}
:param kernel_size_in: kernel size for r {l}) -> r {l} new
:param kernel_size_out: kernel size for (u {l-1}, r {l}) -> r {l-1}
:param up_stride_in: stride for transposed conv r {l}) -> r {l} new
:param stride_out: stride for (u {l-1}, r {l}) -> r {l-1}
:param up_padding_in: padding for transposed convolution r {l} -> r {l} new
:param output_padding: output padding for transposed convolution r {l} -> r {l} new
:param padding_out: padding for (u {l-1}, r {l}) -> r {l-1}
:param activation_in: activation layer for r {l}) -> r {l} new
:param activation_out: activation layer for (u {l-1}, r {l}) -> r {l-1}
:param norm_in: normalization layer for r {l}) -> r {l} new
:param norm_out: normalization layer for (u {l-1}, r {l}) -> r {l-1}
"""
super(RecovecyBlock, self).__init__()
self.in_upconv = upconv_block(
in_channels=in_channels_r,
out_channels=out_channels_r,
kernel_size=kernel_size_in,
stride=up_stride_in,
padding=up_padding_in,
output_padding=output_padding,
norm=norm_in,
activation=activation_in
)
self.out_conv = conv_block(
in_channels=out_channels_r + in_channels_u,
out_channels=out_channels,
kernel_size=kernel_size_out,
stride=stride_out,
padding=padding_out,
norm=norm_out,
activation=activation_out
)
def __init__(self,
in_channels_m, out_channels_m,
in_channels_e, out_channels_e,
kernel_size, stride, padding,
activation='relu', norm='none'):
super(SimpleMADF, self).__init__()
self.conv_m = conv_block(
in_channels=in_channels_m,
out_channels=out_channels_m,
kernel_size=kernel_size,
stride=stride,
padding=padding,
activation=activation,
norm='none')
self.conv_e = conv_block(
in_channels=in_channels_e + in_channels_m,
out_channels=out_channels_e,
kernel_size=kernel_size,
stride=stride,
padding=padding,
activation=activation,
norm=norm)
def __init__(self,
in_channels_1, in_channels_2, out_channels,
kernel_size_1, kernel_size_2,
stride_1, up_stride_2,
padding_1, up_padding_2, output_padding=0,
activation_in='relu', activation_out='lrelu',
norm_in='bn', norm_out='none'):
"""
1 convolution - from f {l-1}{r-1}
2 transposed convolution - from f {l} {k}
3 convolution - the one to be summed with result of f {l}{k} (2) convolution
4 convolution - the one to be producted with result of f {l}{k} (2) convolution
:param in_channels_1: Input channels of the 1st convolution layer
:param in_channels_2: Input channels of the 2st convolution layer
:param out_channels: Output channels of the 1st, 2st, 3rd, 4th convolution layers
:param kernel_size_1: Kernel size of 1st convolution layer
:param kernel_size_2: Kernel size of 2nd transposed convolution layer
:param stride_1: Stride of 1st convolution layer
:param up_stride_2: Stride of 2nd transposed convolution layer
:param padding_1: Padding of 1st convolution layer
:param up_padding_2: Padding of 2nd transposed convolution layer
:param output_padding: Output padding of 2nd transposed convolution layer
:param activation_in: Activation layer of 1st convolution layer
:param activation_out: Activation layer 2nd transposed convolution layer
:param norm_in: Normalization layer of 1st convolution layer
:param norm_out: Normalization layer of 2nd transposed convolution layer
"""
super(RefinementBlock, self).__init__()
self.conv_1 = conv_block(
in_channels=in_channels_1,
out_channels=out_channels,
kernel_size=kernel_size_1,
stride=stride_1,
padding=padding_1,
norm='none',
activation=activation_in
)
self.upconv_2 = upconv_block(
in_channels=in_channels_2,
out_channels=out_channels,
kernel_size=kernel_size_2,
stride=up_stride_2,
padding=up_padding_2,
output_padding=output_padding,
norm=norm_in,
activation='none'
)
self.conv_3 = conv_block(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
stride=1,
padding=1,
norm='none',
activation='none'
)
self.conv_4 = conv_block(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
stride=1,
padding=1,
norm='none',
activation='none'
)
self.out_act = _activation(act_type=activation_out)
self.out_norm = _norm(norm_type=norm_out, channels=out_channels)
def __init__(self,
in_channels_m, out_channels_m,
in_channels_e, out_channels_e,
kernel_size_m, kernel_size_e,
stride_m, stride_e,
padding_m, padding_e,
activation_m='relu', activation_e='relu',
norm_m='none', norm_e='bn',
device=torch.device('cpu')):
"""
:param in_channels_m: input channels of mask layer - m {l-1}
:param out_channels_m: output channels of mask layer - m {l}
:param in_channels_e: input chanels of image layer - e {l-1}
:param out_channels_e: output channels of image layer - e {l}
:param kernel_size_m: kernel size for transformation m {l-1} -> m {l}
:param kernel_size_e: kernel size for transformation e {l-1} -> e {l}
and for kernel creation m {l} -> kernel for e {l} convolution
:param stride_m: stride for m {l-1} -> m {l} transformation
:param stride_e: stride for e {l-1} -> e {l} transformation
:param padding_m: padding for m {l-1} -> m {l} transformation
:param padding_e: padding for e {l-1} -> e {l} transformation
:param activation_m: activation_m for m {l-1} -> m {l} transformation
:param activation_e: activation_e for e {l-1} -> e {l} transformation
"""
super(MADF, self).__init__()
self.in_channels_m = in_channels_m
self.out_channels_m = out_channels_m
self.in_channels_e = in_channels_e
self.out_channels_e = out_channels_e
self.kernel_size_e = kernel_size_e
self.kernel_size_m = kernel_size_m
self.padding_e = padding_e
self.padding_m = padding_m
self.stride_e = stride_e
self.stride_m = stride_m
self.activation_m = activation_m
self.activation_e = activation_e
self.norm_m = norm_m
self.norm_e = norm_e
self.conv_m = conv_block(
in_channels=in_channels_m,
out_channels=out_channels_m,
kernel_size=kernel_size_m,
stride=stride_m,
padding=padding_m,
activation=activation_m,
norm=norm_m)
self.conv_filters = conv_block(
in_channels=out_channels_m,
out_channels=in_channels_e * kernel_size_e *
out_channels_e * kernel_size_e,
kernel_size=1,
stride=1,
padding=0,
activation="none",
norm='none')
self.device = device
self.activation_e = _activation(activation_e)
self.norm = _norm(norm_e, out_channels_e)
def __init__(self,
in_nc=4,
out_nc=3,
res_blocks_num=8,
c_num=48,
dmfb_blocks_num=8,
norm='in',
activation='relu',
device=None):
super(FineGenerator, self).__init__()
self.encoder = nn.ModuleList([
# [4, 256, 256]
conv_block(in_channels=in_nc,
out_channels=c_num,
kernel_size=5,
stride=1,
padding=2,
norm=norm,
activation=activation),
# [c_num, 256, 256] -> decoder
conv_block(in_channels=c_num,
out_channels=2 * c_num,
kernel_size=3,
stride=2,
padding=1,
norm=norm,
activation=activation),
# [c_num * 2, 128, 128]
conv_block(in_channels=2 * c_num,
out_channels=2 * c_num,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [c_num * 2, 128, 128] -> decoder
conv_block(in_channels=2 * c_num,
out_channels=4 * c_num,
kernel_size=3,
stride=2,
padding=1,
norm=norm,
activation=activation),
# [c_num * 4, 64, 64]
conv_block(in_channels=4 * c_num,
out_channels=4 * c_num,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [c_num * 4, 64, 64]
conv_block(in_channels=4 * c_num,
out_channels=4 * c_num,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [c_num * 4, 64, 64]
])
self.fine_bn = FineBottleneck(c_num=4 * c_num,
dmfb_blocks_num=dmfb_blocks_num,
res_blocks_num=res_blocks_num,
device=device)
self.decoder = nn.ModuleList([
# [c_num * 4, 64, 64]
conv_block(in_channels=c_num * 4,
out_channels=c_num * 4,
kernel_size=3,
stride=1,
padding=1),
# [c_num * 4, 64, 64]
conv_block(in_channels=c_num * 4,
out_channels=c_num * 4,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [c_num * 4, 64, 64]
upconv_block(in_channels=c_num * 4,
out_channels=c_num * 2,
kernel_size=3,
stride=2,
output_padding=1,
padding=get_pad_tp(64, 64, [1, 1], [3, 3], [2, 2],
[1, 1]),
norm=norm,
activation=activation),
# encoder 3 + skip [c_num * 2, 128, 128] -> decoder 4
# [c_num * 4, 128, 128]
conv_block(in_channels=c_num * 4,
out_channels=c_num * 2,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [c_num * 2, 128, 128]
upconv_block(in_channels=c_num * 2,
out_channels=c_num,
kernel_size=3,
stride=2,
output_padding=1,
padding=get_pad_tp(128, 128, [1, 1], [3, 3], [2, 2],
[1, 1]),
norm=norm,
activation=activation),
# encoder 1 + skip [c_num, 256, 256] -> decoder 6
# [c_num, 256, 256]
conv_block(in_channels=c_num * 2,
out_channels=c_num // 2,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [c_num//2, 256, 256]
conv_block(in_channels=c_num // 2,
out_channels=out_nc,
kernel_size=3,
stride=1,
padding=1,
norm='none',
activation='tanh'),
# [out_nc, 256, 256]
])
def __init__(self,
c_num,
res_blocks_num=8,
dmfb_blocks_num=8,
activateion="relu",
norm="in",
device=None):
super(FineBottleneck, self).__init__()
dmfb_blocks = []
for i in range(dmfb_blocks_num):
# [c_num, 64, 64]
dmfb_blocks.append(DMFB(in_channels=c_num))
self.dmfb_seq = nn.Sequential(*dmfb_blocks)
res_blocks = []
for i in range(res_blocks_num):
# [c_num, 64, 64]
block = B.ResConv(channels=c_num,
kernel_size=3,
dilation=2,
padding=2)
res_blocks.append(block)
self.res_seq = nn.Sequential(*res_blocks)
# Contextual attention
self.contextual_attention = ContextualAttention(ksize=3,
stride=1,
rate=2,
fuse_k=3,
softmax_scale=10,
fuse=True,
device=device)
self.hypergraph = HypergraphConv(in_channels=c_num,
out_channels=c_num,
filters=256,
edges=256,
height=64,
width=64)
self.out_1 = nn.Sequential(
conv_block(in_channels=3 * c_num,
out_channels=c_num,
kernel_size=1,
stride=1,
padding=0,
norm=norm,
activation=activateion,
pad_type="zero"),
conv_block(in_channels=c_num,
out_channels=c_num,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activateion,
pad_type="zero"))
self.out = nn.Sequential(
conv_block(in_channels=2 * c_num,
out_channels=c_num,
kernel_size=1,
stride=1,
padding=0,
norm=norm,
activation=activateion,
pad_type="zero"),
conv_block(in_channels=c_num,
out_channels=c_num,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activateion,
pad_type="zero"))
def __init__(self,
in_nc=4,
c_num=48,
out_nc=3,
res_blocks_num=8,
norm="in",
activation="relu"):
super(CoarseGenerator, self).__init__()
self.encoder_coarse = nn.ModuleList([
# [4, 256, 256]
conv_block(in_nc,
c_num,
kernel_size=5,
stride=1,
padding=2,
norm=norm,
activation=activation),
# [c_num, 256, 256] -> decoder_6
conv_block(c_num,
c_num * 2,
kernel_size=3,
stride=2,
padding=1,
norm=norm,
activation=activation),
# [c_num * 2, 128, 128]
conv_block(c_num * 2,
c_num * 2,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [c_num * 2, 128, 128] -> decoder_4
conv_block(c_num * 2,
c_num * 4,
kernel_size=3,
stride=2,
padding=1,
norm=norm,
activation=activation),
# [c_num * 4, 64, 64]
conv_block(c_num * 4,
c_num * 4,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [c_num * 4, 64, 64]
conv_block(c_num * 4,
c_num * 4,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation)
# [c_num * 4, 64, 64]]
])
blocks = []
for _ in range(res_blocks_num):
block = B.ResConv(4 * c_num, kernel_size=3, dilation=2,
padding=2) # [192, 64, 64]
blocks.append(block)
self.coarse_bn = nn.Sequential(*blocks)
self.decoder_coarse = nn.ModuleList([
conv_block(c_num * 4,
c_num * 4,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [c_num * 4, 64, 64]
conv_block(c_num * 4,
c_num * 4,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [c_num * 4, 64, 64]
upconv_block(c_num * 4,
c_num * 2,
kernel_size=3,
stride=2,
output_padding=1,
padding=get_pad_tp(64, 64, [1, 1], [3, 3], [2, 2],
[1, 1]),
norm=norm,
activation=activation),
# [c_num * 2, 128, 128] + skip [c_num * 2, 128, 128]
conv_block(c_num * 4,
c_num * 2,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [c_num * 2, 128, 128]
upconv_block(c_num * 2,
c_num,
kernel_size=3,
stride=2,
output_padding=1,
padding=get_pad_tp(128, 128, [1, 1], [3, 3], [2, 2],
[1, 1]),
norm=norm,
activation=activation),
# [c_num, 256, 256] + skip [c_num, 256, 256]
conv_block(c_num * 2,
c_num,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [c_num, 256, 256]
conv_block(c_num,
c_num // 2,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation)
# [c_num // 2, 256, 256]
])
self.out_coarse = nn.Sequential(
conv_block(c_num // 2,
out_nc,
3,
stride=1,
padding=1,
norm='none',
activation='tanh'))
def __init__(self,
in_nc=4,
out_nc=3,
nf=48,
norm="in",
activation="relu",
res_blocks_num=8,
dmfb_block_num=4,
device=torch.device('cuda')):
"""
:param in_nc: in channels number
:param out_nc: out channels number
:param nf: number of intermediete features
:param res_blocks_num: number of Residual blocks in fine and coarse roots
:param dmfb_block_num: nunumber of DMFB blocks in fine route
"""
super(InpaintingGenerator, self).__init__()
# [4, 256, 256]
self.encoder_coarse = nn.ModuleList([
# [48, 256, 256] -> decoder_6
conv_block(in_nc,
nf,
kernel_size=5,
stride=1,
padding=2,
norm=norm,
activation=activation),
# [96, 128, 128]
conv_block(nf,
nf * 2,
kernel_size=3,
stride=2,
padding=1,
norm=norm,
activation=activation),
# [96, 128, 128] -> decoder_4
conv_block(nf * 2,
nf * 2,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [192, 64, 64]
conv_block(nf * 2,
nf * 4,
kernel_size=3,
stride=2,
padding=1,
norm=norm,
activation=activation),
# [192, 64, 64]
conv_block(nf * 4,
nf * 4,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [192, 64, 64]
conv_block(nf * 4,
nf * 4,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation)
])
self.encoder_fine = nn.ModuleList([
# [48, 256, 256] -> decoder_6
conv_block(in_nc,
nf,
kernel_size=5,
stride=1,
padding=2,
norm=norm,
activation=activation),
# [96, 128, 128]
conv_block(nf,
nf * 2,
kernel_size=3,
stride=2,
padding=1,
norm=norm,
activation=activation),
# [96, 128, 128] -> decoder_4
conv_block(nf * 2,
nf * 2,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [192, 64, 64]
conv_block(nf * 2,
nf * 4,
kernel_size=3,
stride=2,
padding=1,
norm=norm,
activation=activation),
# [192, 64, 64]
conv_block(nf * 4,
nf * 4,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [192, 64, 64]
conv_block(nf * 4,
nf * 4,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation)
])
blocks = []
for _ in range(res_blocks_num):
block = B.ResConv(4 * nf, kernel_size=3, dilation=2,
padding=2) # [192, 64, 64]
blocks.append(block)
self.coarse = nn.Sequential(*blocks)
self.fine = FineBottleneck(4 * nf,
res_blocks_num,
dmfb_block_num,
attention_mode="self")
self.decoder_coarse = nn.ModuleList([
# [192, 64, 64]
conv_block(nf * 4,
nf * 4,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [192, 64, 64]
conv_block(nf * 4,
nf * 4,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [96, 128, 128]
upconv_block(nf * 4,
nf * 2,
kernel_size=3,
stride=2,
output_padding=1,
padding=get_pad_tp(64, 64, [1, 1], [3, 3], [2, 2],
[1, 1]),
norm=norm,
activation=activation),
# [96, 128, 128]
conv_block(nf * 4,
nf * 2,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [48, 256, 256]
upconv_block(nf * 2,
nf,
kernel_size=3,
stride=2,
output_padding=1,
padding=get_pad_tp(128, 128, [1, 1], [3, 3], [2, 2],
[1, 1]),
norm=norm,
activation=activation),
# [48, 256, 256]
conv_block(nf * 2,
nf,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [24, 256, 256]
conv_block(nf,
nf // 2,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation)
])
self.decoder_fine = nn.ModuleList([
# [192, 64, 64]
conv_block(nf * 4,
nf * 4,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [192, 64, 64]
conv_block(nf * 4,
nf * 4,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [96, 128, 128]
upconv_block(nf * 4,
nf * 2,
kernel_size=3,
stride=2,
output_padding=1,
padding=get_pad_tp(64, 64, [1, 1], [3, 3], [2, 2],
[1, 1]),
norm=norm,
activation=activation),
# [96, 128, 128]
conv_block(nf * 4,
nf * 2,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [48, 256, 256]
upconv_block(nf * 2,
nf,
kernel_size=3,
stride=2,
output_padding=1,
padding=get_pad_tp(128, 128, [1, 1], [3, 3], [2, 2],
[1, 1]),
norm=norm,
activation=activation),
# [48, 256, 256]
conv_block(nf * 2,
nf,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation),
# [24, 256, 256]
conv_block(nf,
nf // 2,
kernel_size=3,
stride=1,
padding=1,
norm=norm,
activation=activation)
])
self.out_coarse = nn.Sequential(
conv_block(nf // 2,
out_nc,
3,
stride=1,
padding=1,
norm='none',
activation='tanh'))
self.out_fine = nn.Sequential(
conv_block(nf // 2,
out_nc,
3,
stride=1,
padding=1,
norm='none',
activation='tanh'))
self.device = device
def __init__(self, in_nc=4, c_num=32, device=None):
super(FineGenerator, self).__init__()
self.conv_seq = nn.Sequential(
# [4, 256, 256]
conv_block(in_channels=in_nc,
out_channels=c_num,
kernel_size=5,
stride=1,
padding=2),
# [cnum, 256, 256]
conv_block(in_channels=c_num,
out_channels=2 * c_num,
kernel_size=3,
stride=2,
padding=1),
# [cnum * 2, 128, 128]
conv_block(in_channels=2 * c_num,
out_channels=2 * c_num,
kernel_size=3,
stride=2,
padding=1),
# [cnum * 2, 64, 64]
conv_block(in_channels=2 * c_num,
out_channels=4 * c_num,
kernel_size=3,
stride=1,
padding=1),
# [cnum * 4, 64, 64]
conv_block(in_channels=4 * c_num,
out_channels=4 * c_num,
kernel_size=3,
stride=1,
padding=1),
# [cnum * 4, 64, 64]
# dilation
conv_block(in_channels=4 * c_num,
out_channels=4 * c_num,
kernel_size=3,
stride=1,
padding=2,
dilation=2),
# [cnum * 4, 64, 64]
conv_block(in_channels=4 * c_num,
out_channels=4 * c_num,
kernel_size=3,
stride=1,
padding=4,
dilation=4),
# [cnum * 4, 64, 64]
conv_block(in_channels=4 * c_num,
out_channels=4 * c_num,
kernel_size=3,
stride=1,
padding=8,
dilation=8),
# [cnum * 4, 64, 64]
conv_block(in_channels=4 * c_num,
out_channels=4 * c_num,
kernel_size=3,
stride=1,
padding=16,
dilation=16)
# [cnum * 4, 64, 64]
)
self.before_attn = nn.Sequential(
# [4, 256, 256]
conv_block(in_channels=in_nc,
out_channels=c_num,
kernel_size=5,
stride=1,
padding=2),
# [cnum, 256, 256]
conv_block(in_channels=c_num,
out_channels=2 * c_num,
kernel_size=3,
stride=2,
padding=1),
# [cnum * 2, 128, 128]
conv_block(in_channels=2 * c_num,
out_channels=2 * c_num,
kernel_size=3,
stride=2,
padding=1),
# [cnum * 2, 64, 64]
conv_block(in_channels=2 * c_num,
out_channels=4 * c_num,
kernel_size=3,
stride=1,
padding=1),
# [cnum * 4, 64, 64]
conv_block(in_channels=4 * c_num,
out_channels=4 * c_num,
kernel_size=3,
stride=1,
padding=1),
# [cnum * 4, 64, 64]
)
# Contextual attention
self.contextual_attention = ContextualAttention(ksize=3,
stride=1,
rate=2,
fuse_k=3,
softmax_scale=10,
fuse=True,
device=device)
# [cnum * 4, 64, 64]
self.after_attn = nn.Sequential(
conv_block(in_channels=4 * c_num,
out_channels=4 * c_num,
kernel_size=3,
stride=1,
padding=1),
# [cnum * 4, 64, 64]
conv_block(in_channels=4 * c_num,
out_channels=4 * c_num,
kernel_size=3,
stride=1,
padding=1),
# [cnum * 4, 64, 64]
)
self.all_conv = nn.Sequential(
# concatenated input [cnum * 8, 64, 64]
conv_block(in_channels=8 * c_num,
out_channels=4 * c_num,
kernel_size=3,
stride=1,
padding=1),
# [cnum * 4, 64, 64]
conv_block(in_channels=4 * c_num,
out_channels=4 * c_num,
kernel_size=3,
stride=1,
padding=1),
# [cnum * 4, 64, 64]
upconv_block(in_channels=4 * c_num,
out_channels=2 * c_num,
kernel_size=3,
stride=2,
padding=1,
norm='in',
output_padding=1),
# [cnum * 2, 128, 128]
conv_block(in_channels=2 * c_num,
out_channels=2 * c_num,
kernel_size=3,
stride=1,
padding=1),
# [cnum * 2, 128, 128]
upconv_block(in_channels=2 * c_num,
out_channels=c_num,
kernel_size=3,
stride=2,
padding=1,
norm='in',
output_padding=1),
# [cnum, 256, 256]
conv_block(in_channels=c_num,
out_channels=c_num // 2,
kernel_size=3,
stride=1,
padding=1),
# [cnum * 8, 256, 256]
conv_block(in_channels=c_num // 2,
out_channels=3,
kernel_size=3,
stride=1,
padding=1,
activation='tanh'),
)
def __init__(self,
in_channels_m=1,
in_channels_e=3,
device=torch.device('cpu')):
super(CoarseEncoder, self).__init__()
self.madf_seq = nn.ModuleList([
# im - [3, 256, 256]
# mask - [1, 256, 256]
B.SimpleMADF(in_channels_m=in_channels_m,
out_channels_m=2,
in_channels_e=in_channels_e,
out_channels_e=16,
kernel_size=5,
stride=2,
padding=2),
# im - [16, 128, 128]
# mask - [2, 128, 128]
B.SimpleMADF(in_channels_m=2,
out_channels_m=4,
in_channels_e=16,
out_channels_e=32,
kernel_size=3,
stride=2,
padding=1),
# im - [32, 64, 64]
# mask - [4, 64, 64]
B.SimpleMADF(in_channels_m=4,
out_channels_m=8,
in_channels_e=32,
out_channels_e=64,
kernel_size=3,
stride=2,
padding=1),
# im - [64, 32, 32]
# mask - [8, 32, 32]
B.SimpleMADF(in_channels_m=8,
out_channels_m=16,
in_channels_e=64,
out_channels_e=128,
kernel_size=3,
stride=2,
padding=1),
# im - [128, 16, 16]
# mask - [16, 16, 16]
])
self.up_seq = nn.ModuleList([
conv_block(in_channels=3,
out_channels=16,
kernel_size=3,
stride=1,
padding=1,
activation='relu',
norm='none'),
conv_block(in_channels=16,
out_channels=16,
kernel_size=3,
stride=1,
padding=1,
activation='relu',
norm='none'),
conv_block(in_channels=32,
out_channels=32,
kernel_size=3,
stride=1,
padding=1,
activation='relu',
norm='none'),
conv_block(in_channels=64,
out_channels=64,
kernel_size=3,
stride=1,
padding=1,
activation='relu',
norm='none'),
conv_block(in_channels=128,
out_channels=128,
kernel_size=3,
stride=1,
padding=1,
activation='relu',
norm='none')
])