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"""
This file only for testing mask regularzation.
If it works, it will be merged with `layers.py`.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class AADLayer(nn.Module):
def __init__(self, c_x, attr_c, c_id=256):
super(AADLayer, self).__init__()
self.attr_c = attr_c
self.c_id = c_id
self.c_x = c_x
self.conv1 = nn.Conv2d(
attr_c, c_x, kernel_size=1, stride=1, padding=0, bias=True
)
self.conv2 = nn.Conv2d(
attr_c, c_x, kernel_size=1, stride=1, padding=0, bias=True
)
self.fc1 = nn.Linear(c_id, c_x)
self.fc2 = nn.Linear(c_id, c_x)
self.norm = nn.InstanceNorm2d(c_x, affine=False)
self.conv_h = nn.Conv2d(c_x, 1, kernel_size=1, stride=1, padding=0, bias=True)
def forward(self, h_in, z_attr, z_id):
# h_in cxnxn
# zid 256x1x1
# zattr cxnxn
h = self.norm(h_in)
gamma_attr = self.conv1(z_attr)
beta_attr = self.conv2(z_attr)
gamma_id = self.fc1(z_id)
beta_id = self.fc2(z_id)
A = gamma_attr * h + beta_attr
gamma_id = gamma_id.reshape(h.shape[0], self.c_x, 1, 1).expand_as(h)
beta_id = beta_id.reshape(h.shape[0], self.c_x, 1, 1).expand_as(h)
I = gamma_id * h + beta_id
M = torch.sigmoid(self.conv_h(h))
out = (torch.ones_like(M).to(M.device) - M) * A + M * I
return out, torch.mean(torch.ones_like(M).to(M.device) - M, dim=[1, 2, 3])
class AAD_ResBlk(nn.Module):
def __init__(self, cin, cout, c_attr, c_id=256):
super(AAD_ResBlk, self).__init__()
self.cin = cin
self.cout = cout
self.AAD1 = AADLayer(cin, c_attr, c_id)
self.conv1 = nn.Conv2d(cin, cin, kernel_size=3, stride=1, padding=1, bias=False)
self.relu1 = nn.ReLU(inplace=True)
self.AAD2 = AADLayer(cin, c_attr, c_id)
self.conv2 = nn.Conv2d(
cin, cout, kernel_size=3, stride=1, padding=1, bias=False
)
self.relu2 = nn.ReLU(inplace=True)
if cin != cout:
self.AAD3 = AADLayer(cin, c_attr, c_id)
self.conv3 = nn.Conv2d(
cin, cout, kernel_size=3, stride=1, padding=1, bias=False
)
self.relu3 = nn.ReLU(inplace=True)
def forward(self, h, z_attr, z_id):
x, m1_ = self.AAD1(h, z_attr, z_id)
x = self.relu1(x)
x = self.conv1(x)
x, m2_ = self.AAD2(x, z_attr, z_id)
x = self.relu2(x)
x = self.conv2(x)
m = m1_ + m2_
if self.cin != self.cout:
h, m3_ = self.AAD3(h, z_attr, z_id)
h = self.relu3(h)
h = self.conv3(h)
m += m3_
x = x + h
return x, m
def weight_init(m):
if isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.001)
m.bias.data.zero_()
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight.data)
if isinstance(m, nn.ConvTranspose2d):
nn.init.xavier_normal_(m.weight.data)
def conv4x4(in_c, out_c, norm=nn.BatchNorm2d):
return nn.Sequential(
nn.Conv2d(
in_channels=in_c,
out_channels=out_c,
kernel_size=4,
stride=2,
padding=1,
bias=False,
),
norm(out_c),
nn.LeakyReLU(0.1, inplace=True),
)
class deconv4x4(nn.Module):
def __init__(self, in_c, out_c, norm=nn.BatchNorm2d):
super(deconv4x4, self).__init__()
self.deconv = nn.ConvTranspose2d(
in_channels=in_c,
out_channels=out_c,
kernel_size=4,
stride=2,
padding=1,
bias=False,
)
self.bn = norm(out_c)
self.lrelu = nn.LeakyReLU(0.1, inplace=True)
def forward(self, input, skip):
x = self.deconv(input)
x = self.bn(x)
x = self.lrelu(x)
return torch.cat((x, skip), dim=1)
class MLAttrEncoder(nn.Module):
def __init__(self, finetune=False, downup=False):
super(MLAttrEncoder, self).__init__()
self.downup = downup
if self.downup:
self.conv00 = conv4x4(3, 16)
self.conv01 = conv4x4(16, 32)
self.deconv7 = deconv4x4(64, 16)
self.conv1 = conv4x4(3, 32)
self.conv2 = conv4x4(32, 64)
self.conv3 = conv4x4(64, 128)
self.conv4 = conv4x4(128, 256)
self.conv5 = conv4x4(256, 512)
self.conv6 = conv4x4(512, 1024)
self.conv7 = conv4x4(1024, 1024)
self.deconv1 = deconv4x4(1024, 1024)
self.deconv2 = deconv4x4(2048, 512)
self.deconv3 = deconv4x4(1024, 256)
self.deconv4 = deconv4x4(512, 128)
self.deconv5 = deconv4x4(256, 64)
self.deconv6 = deconv4x4(128, 32)
self.apply(weight_init)
self.finetune = finetune
if finetune:
for name, param in self.named_parameters():
param.requires_grad = False
if self.downup:
self.conv00.requires_grad_(True)
self.conv01.requires_grad_(True)
self.deconv7.requires_grad_(True)
def forward(self, Xt):
if self.downup:
feat0 = self.conv00(Xt) # (16,256,256)
feat1 = self.conv01(feat0) # (32,128,128)
else:
feat0 = None
feat1 = self.conv1(Xt)
# 32x128x128
feat2 = self.conv2(feat1)
# 64x64x64
feat3 = self.conv3(feat2)
# 128x32x32
feat4 = self.conv4(feat3)
# 256x16xx16
feat5 = self.conv5(feat4)
# 512x8x8
feat6 = self.conv6(feat5)
# 1024x4x4
if self.downup:
z_attr1 = self.conv7(feat6)
# 1024x2x2
z_attr2 = self.deconv1(z_attr1, feat6)
z_attr3 = self.deconv2(z_attr2, feat5)
z_attr4 = self.deconv3(z_attr3, feat4)
z_attr5 = self.deconv4(z_attr4, feat3)
z_attr6 = self.deconv5(z_attr5, feat2)
z_attr7 = self.deconv6(z_attr6, feat1) # (128,64,64)+(32,128,128)->(64,128,128)
z_attr8 = self.deconv7(z_attr7, feat0) # (64,128,128)+(16,256,256)->(32,256,256)
z_attr9 = F.interpolate(
z_attr8, scale_factor=2, mode="bilinear", align_corners=True
) # (32,512,512)
return (
z_attr1,
z_attr2,
z_attr3,
z_attr4,
z_attr5,
z_attr6,
z_attr7,
z_attr8,
z_attr9
)
else:
z_attr1 = self.conv7(feat6)
# 1024x2x2
z_attr2 = self.deconv1(z_attr1, feat6)
z_attr3 = self.deconv2(z_attr2, feat5)
z_attr4 = self.deconv3(z_attr3, feat4)
z_attr5 = self.deconv4(z_attr4, feat3)
z_attr6 = self.deconv5(z_attr5, feat2)
z_attr7 = self.deconv6(z_attr6, feat1)
z_attr8 = F.interpolate(
z_attr7, scale_factor=2, mode="bilinear", align_corners=True
)
return (
z_attr1,
z_attr2,
z_attr3,
z_attr4,
z_attr5,
z_attr6,
z_attr7,
z_attr8,
)
class AADGenerator(nn.Module):
def __init__(self, c_id=256, finetune=False, downup=False):
super(AADGenerator, self).__init__()
self.up1 = nn.ConvTranspose2d(c_id, 1024, kernel_size=2, stride=1, padding=0)
self.AADBlk1 = AAD_ResBlk(1024, 1024, 1024, c_id)
self.AADBlk2 = AAD_ResBlk(1024, 1024, 2048, c_id)
self.AADBlk3 = AAD_ResBlk(1024, 1024, 1024, c_id)
self.AADBlk4 = AAD_ResBlk(1024, 512, 512, c_id)
self.AADBlk5 = AAD_ResBlk(512, 256, 256, c_id)
self.AADBlk6 = AAD_ResBlk(256, 128, 128, c_id)
self.AADBlk7 = AAD_ResBlk(128, 64, 64, c_id)
self.AADBlk8 = AAD_ResBlk(64, 3, 64, c_id)
self.downup = downup
if downup:
self.AADBlk8_0 = AAD_ResBlk(64, 32, 32, c_id)
self.AADBlk8_1 = AAD_ResBlk(32, 3, 32, c_id)
self.apply(weight_init)
if finetune:
for name, param in self.named_parameters():
param.requires_grad = False
self.AADBlk8_0.requires_grad_(True)
self.AADBlk8_1.requires_grad_(True)
def forward(self, z_attr, z_id):
m = self.up1(z_id.reshape(z_id.shape[0], -1, 1, 1))
scale= z_attr[0].shape[2] // 2 # adaptive support for 512x512, 1024x1024
m = F.interpolate(m, scale_factor=scale, mode='bilinear', align_corners=True)
m2, m2_ = self.AADBlk1(m, z_attr[0], z_id)
m2 = F.interpolate(
m2,
scale_factor=2,
mode="bilinear",
align_corners=True,
)
m3, m3_ = self.AADBlk2(m2, z_attr[1], z_id)
m3 = F.interpolate(
m3,
scale_factor=2,
mode="bilinear",
align_corners=True,
)
m4, m4_ = self.AADBlk3(m3, z_attr[2], z_id)
m4 = F.interpolate(
m4,
scale_factor=2,
mode="bilinear",
align_corners=True,
)
m5, m5_ = self.AADBlk4(m4, z_attr[3], z_id)
m5 = F.interpolate(
m5,
scale_factor=2,
mode="bilinear",
align_corners=True,
)
m6, m6_ = self.AADBlk5(m5, z_attr[4], z_id)
m6 = F.interpolate(
m6,
scale_factor=2,
mode="bilinear",
align_corners=True,
)
m7, m7_ = self.AADBlk6(m6, z_attr[5], z_id)
m7 = F.interpolate(
m7,
scale_factor=2,
mode="bilinear",
align_corners=True,
)
m8, m8_ = self.AADBlk7(m7, z_attr[6], z_id)
m8 = F.interpolate(
m8,
scale_factor=2,
mode="bilinear",
align_corners=True,
)
if self.downup:
y0, m9_ = self.AADBlk8_0(m8, z_attr[7], z_id)
y0 = F.interpolate(y0, scale_factor=2, mode='bilinear', align_corners=True)
y1, m10_ = self.AADBlk8_1(y0, z_attr[8], z_id)
y = torch.tanh(y1)
else:
y, m9_ = self.AADBlk8(m8, z_attr[7], z_id)
y = torch.tanh(y)
return y # , m # yuange
class AEI_Net(nn.Module):
def __init__(self, c_id=512, finetune=False, downup=False):
super(AEI_Net, self).__init__()
self.encoder = MLAttrEncoder(finetune=finetune, downup=downup)
self.generator = AADGenerator(c_id, finetune=finetune, downup=downup)
def forward(self, Xt, z_id):
attr = self.encoder(Xt)
Y = self.generator(attr, z_id) # yuange
return Y, attr
def get_attr(self, X):
return self.encoder(X)
def trainable_params(self):
train_params = []
for param in self.parameters():
if param.requires_grad:
train_params.append(param)
return train_params
if __name__ == "__main__":
aie = AEI_Net(512).eval()
x = aie(torch.randn(1, 3, 512, 512), torch.randn(1, 512))
# def numel(m: torch.nn.Module, only_trainable: bool = False):
# """
# returns the total number of parameters used by `m` (only counting
# shared parameters once); if `only_trainable` is True, then only
# includes parameters with `requires_grad = True`
# """
# parameters = list(m.parameters())
# if only_trainable:
# parameters = [p for p in parameters if p.requires_grad]
# unique = {p.data_ptr(): p for p in parameters}.values()
# return sum(p.numel() for p in unique)
#
#
# print(numel(aie, True))
# print(x[0].size())
# print(len(x[-1]))
import thop
img = torch.randn(1, 3, 256, 256)
latent = torch.randn(1, 512)
net = aie
flops, params = thop.profile(net, inputs=(img, latent), verbose=False)
print('#Params=%.2fM, GFLOPS=%.2f' % (params / 1e6, flops / 1e9))
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