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import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
class VanillaConv(nn.Module):
def __init__(
self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1,
groups=1, bias=True, norm="SN", activation=nn.LeakyReLU(0.2, inplace=True)
):
super().__init__()
if padding == -1:
if isinstance(kernel_size, int):
kernel_size = (kernel_size, kernel_size)
if isinstance(dilation, int):
dilation = (dilation, dilation)
self.padding = tuple(((np.array(kernel_size) - 1) * np.array(dilation)) // 2) if padding == -1 else padding
self.featureConv = nn.Conv2d(
in_channels, out_channels, kernel_size,
stride, self.padding, dilation, groups, bias)
self.norm = norm
if norm == "BN":
self.norm_layer = nn.BatchNorm2d(out_channels)
elif norm == "IN":
self.norm_layer = nn.InstanceNorm2d(out_channels, track_running_stats=True)
elif norm == "SN":
self.norm = None
self.featureConv = nn.utils.spectral_norm(self.featureConv)
else:
self.norm = None
self.activation = activation
def forward(self, xs):
out = self.featureConv(xs)
if self.activation:
out = self.activation(out)
if self.norm is not None:
out = self.norm_layer(out)
return out
class VanillaDeconv(nn.Module):
def __init__(
self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1,
groups=1, bias=True, norm="SN", activation=nn.LeakyReLU(0.2, inplace=True),
scale_factor=2
):
super().__init__()
self.conv = VanillaConv(
in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, bias, norm, activation)
self.scale_factor = scale_factor
def forward(self, xs):
xs_resized = F.interpolate(xs, scale_factor=self.scale_factor)
return self.conv(xs_resized)
class GatedConv(VanillaConv):
def __init__(
self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1,
groups=1, bias=True, norm="SN", activation=nn.LeakyReLU(0.2, inplace=True)
):
super().__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, bias, norm, activation
)
self.gatingConv = nn.Conv2d(
in_channels, out_channels, kernel_size,
stride, self.padding, dilation, groups, bias)
if norm == 'SN':
self.gatingConv = nn.utils.spectral_norm(self.gatingConv)
self.sigmoid = nn.Sigmoid()
self.store_gated_values = False
def gated(self, mask):
# return torch.clamp(mask, -1, 1)
out = self.sigmoid(mask)
if self.store_gated_values:
self.gated_values = out.detach().cpu()
return out
def forward(self, xs):
gating = self.gatingConv(xs)
feature = self.featureConv(xs)
if self.activation:
feature = self.activation(feature)
out = self.gated(gating) * feature
if self.norm is not None:
out = self.norm_layer(out)
return out
class GatedDeconv(VanillaDeconv):
def __init__(
self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1,
groups=1, bias=True, norm="SN", activation=nn.LeakyReLU(0.2, inplace=True),
scale_factor=2
):
super().__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, bias, norm, activation, scale_factor
)
self.conv = GatedConv(
in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, bias, norm, activation)
class PartialConv(VanillaConv):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1,
groups=1, bias=True, norm="SN", activation=nn.LeakyReLU(0.2, inplace=True)):
super().__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, bias, norm, activation
)
self.mask_sum_conv = nn.Conv2d(1, 1, kernel_size,
stride, self.padding, dilation, groups, False)
nn.init.constant_(self.mask_sum_conv.weight, 1.0)
# mask conv needs not update
for param in self.mask_sum_conv.parameters():
param.requires_grad = False
def forward(self, input_tuple):
# http://masc.cs.gmu.edu/wiki/partialconv
# C(X) = W^T * X + b, C(0) = b, D(M) = 1 * M + 0 = sum(M)
# output = W^T* (M .* X) / sum(M) + b = [C(M .* X) – C(0)] / D(M) + C(0), if sum(M) != 0
# = 0, if sum(M) == 0
inp, mask = input_tuple
# print(inp.shape, mask.shape)
# C(M .* X)
output = self.featureConv(mask * inp)
# C(0) = b
if self.featureConv.bias is not None:
output_bias = self.featureConv.bias.view(1, -1, 1, 1)
else:
output_bias = torch.zeros([1, 1, 1, 1]).to(inp.device)
# D(M) = sum(M)
with torch.no_grad():
mask_sum = self.mask_sum_conv(mask)
# find those sum(M) == 0
no_update_holes = (mask_sum == 0)
# Just to prevent devided by 0
mask_sum_no_zero = mask_sum.masked_fill_(no_update_holes, 1.0)
# output = [C(M .* X) – C(0)] / D(M) + C(0), if sum(M) != 0
# = 0, if sum (M) == 0
output = (output - output_bias) / mask_sum_no_zero + output_bias
output = output.masked_fill_(no_update_holes, 0.0)
# create a new mask with only 1 or 0
new_mask = torch.ones_like(mask_sum)
new_mask = new_mask.masked_fill_(no_update_holes, 0.0)
if self.activation is not None:
output = self.activation(output)
if self.norm is not None:
output = self.norm_layer(output)
return output, new_mask
class PartialDeconv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1,
groups=1, bias=True, norm="SN", activation=nn.LeakyReLU(0.2, inplace=True),
scale_factor=2):
super().__init__()
self.conv = PartialConv(
in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, bias, norm, activation)
self.scale_factor = scale_factor
def forward(self, input_tuple):
inp, mask = input_tuple
inp_resized = F.interpolate(inp, scale_factor=self.scale_factor)
with torch.no_grad():
mask_resized = F.interpolate(mask, scale_factor=self.scale_factor)
return self.conv((inp_resized, mask_resized))
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